Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer

AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mi…

Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measu…

Miniature MOEMS hyperspectral imager with versatile analysis tools

The Fabry-Perot interferometers (FPI) are essential components of many hyperspectral imagers (HSI). While the Piezo-FPI (PFPI) are still very relevant in low volume, high performance applications, the tunable MOEMS FPI (MFPI) technology enables volume-scalable manufacturing, thus having potential to be a major game changer with the advantages of low costs and miniaturization. However, before a FPI can be utilized, it must be integrated with matching optical assembly, driving electronics and imaging sensor. Most importantly, the whole HSI system must be calibrated to account for wide variety of unwanted physical and environmental effects, that significantly influence quality of hyperspectral…

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue sampl…

Hyperspectral imaging reveals spectral differences and can distinguish malignant melanoma from pigmented basal cell carcinomas : A pilot study

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigmented basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ, 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopathological diagnosis. For 2-class classifier (melanocytic tumours vs pigmented basal cell carcinomas) using the majority of…

Discriminating Basal Cell Carcinoma and Bowen’s Disease with Novel Hyperspectral Imaging System and Convolutional Neural Networks

Delineating margins of lentigo maligna using a hyperspectral imaging system

Lentigo maligna (LM) is an in situ form of melanoma which can progress into invasive lentigo maligna melanoma (LMM). Variations in the pigmentation and thus visibility of the tumour make assessment of lesion borders challenging. We tested hyperspectral imaging system (HIS) in in vivo preoperative delineation of LM and LMM margins. We compared lesion margins delineated by HIS with those estimated clinically, and confirmed histologically. A total of 14 LMs and 5 LMMs in 19 patients were included. HIS analysis matched the histopathological analysis in 18/19 (94.7%) cases while in 1/19 (5.3%) cases HIS showed lesion extension not confirmed by histopathology (false positives). Compared to clinic…

Hyperspectral Imaging of Macroinvertebrates : a Pilot Study for Detecting Metal Contamination in Aquatic Ecosystems

The applicability of spectral analysis in detection of freshwater metal contamination was assessed by developing and testing a novel hyperspectral imaging (HSI) application for aquatic insect larvae (Trichoptera: Hydropsychidae). Larvae were first exposed to four different cadmium (Cd) concentrations: 0, 1, 10, and 100 μg L−1 for 96 h. Individual larvae were then preserved in ethanol, inspected with microscopy for the number of anomalies in larval gills, and imaged by hyperspectral camera operating with wavebands between 500 and 850 nm. Three additional larvae from each exposure were analyzed for tissue Cd concentration. Although the larval tissue Cd concentrations correlated positively wit…

Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture

Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire pro…

Using Wave Propagation Simulations and Convolutional Neural Networks to Retrieve Thin Film Thickness from Hyperspectral Images

Ill-posed inversion problems are one of the major challenges when there is a need to combine measurements with the theory and numerical model. In this study, we demonstrate the use of wave propagation simulations to train a convolutional neural network (CNN) for retrieving sub-wavelength thickness profiles of thin film coatings from hyperspectral images. The simulations are produced by solving numerically one-dimensional wave equation with a method based on Discrete Exterior Calculus (DEC). This approach provides a powerful tool to produce large sets of training data for the neural network. CNN was verified by simulated verification sets and measured reflectance spectra, both of which showe…

UAS BASED TREE SPECIES IDENTIFICATION USING THE NOVEL FPI BASED HYPERSPECTRAL CAMERAS IN VISIBLE, NIR AND SWIR SPECTRAL RANGES

Abstract. Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for identifying different species of trees in a forest area. To the best of the authors’ knowledge, this was the first research where stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral technology provided accurate geometric, radiometric and sp…

Spectral imaging from UAVs under varying illumination conditions

Abstract. Rapidly developing unmanned aerial vehicles (UAV) have provided the remote sensing community with a new rapidly deployable tool for small area monitoring. The progress of small payload UAVs has introduced greater demand for light weight aerial payloads. For applications requiring aerial images, a simple consumer camera provides acceptable data. For applications requiring more detailed spectral information about the surface, a new Fabry-Perot interferometer based spectral imaging technology has been developed. This new technology produces tens of successive images of the scene at different wavelength bands in very short time. These images can be assembled in spectral data cubes wit…

Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basin…

Safety of Novel Amino-5-laevulinate Photosensitizer Precursors in Photodynamic Therapy for Healthy Human Skin.

Peer reviewed

Discovering knowledge in various applications with a novel hyperspectral imager

Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks

New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from canc…

Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables

Remote sensing using unmanned aerial vehicle (UAV) -borne sensors is currently a highly interesting approach for the estimation of forest characteristics. 3D remote sensing data from airborne laser scanning or digital stereo photogrammetry enable highly accurate estimation of forest variables related to the volume of growing stock and dimension of the trees, whereas recognition of tree species dominance and proportion of different tree species has been a major complication in remote sensing-based estimation of stand variables. In this study the use of UAV-borne hyperspectral imagery was examined in combination with a high-resolution photogrammetric canopy height model in estimating forest v…

Using VIS/NIR and IR spectral cameras for detecting and separating crime scene details

Detecting invisible details and separating mixed evidence is critical for forensic inspection. If this can be done reliably and fast at the crime scene, irrelevant objects do not require further examination at the laboratory. This will speed up the inspection process and release resources for other critical tasks. This article reports on tests which have been carried out at the University of Jyväskylä in Finland together with the Central Finland Police Department and the National Bureau of Investigation for detecting and separating forensic details with hyperspectral technology. In the tests evidence was sought after at an assumed violent burglary scene with the use of VTT's 500-900 nm wave…

Fotogrammetrisen 3D-latvusmallin ja hyperspektriaineiston käyttö aluetason puustotulkinnassa

Seloste artikkelista Tuominen S., Balazs A., Honkavaara E., Polonen I., Saari H., Hakala T., Viljanen N. (2017). Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica vol. 51 no. 5 article id 7721. https://doi. org/10.14214/sf.7721

Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma

Ablative fractional laser-assisted photodynamic therapy for lentigo maligna: a prospective pilot study.

Background Lentigo maligna (LM) is an in‐situ form of melanoma carrying a risk of progression to invasive lentigo maligna melanoma (LMM). LM poses a clinical challenge, with subclinical extension and high recurrence rates after incomplete surgery. Alternative treatment methods have been investigated with varying results. Photodynamic therapy (PDT) with methylaminolaevulinate (MAL) has already proved promising in this respect. Objectives To investigate the efficacy of ablative fractional laser (AFL)‐assisted PDT with 5‐aminolaevulinic acid nanoemulsion (BF‐200 ALA) for treating LM. Methods In this non‐sponsored, prospective pilot study ten histologically verified LMs were treated with AFL‐as…

Differentiating Malignant from Benign for Melanocytic and Non-melanocytic Skin Tumors : A Pilot Study on Hyperspectral Imaging and Convolutional Neural Networks

Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated …

Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital sur…

Hexyl aminolevulinate, 5‐aminolevulinic acid nanoemulsion and methyl aminolevulinate in photodynamic therapy of non‐aggressive basal cell carcinomas: A non‐sponsored, randomized, prospective and double‐blinded trial

Background In the photodynamic therapy (PDT) of non‐aggressive basal cell carcinomas (BCCs), 5‐aminolevulinic acid nanoemulsion (BF‐200ALA) has shown non‐inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL‐PDT in the treatment of BCCs. Objectives To compare the histological clearance, tolerability (pain and post‐treatment reaction), and cosmetic outcome of MAL, BF‐200 ALA, and low‐concentration HAL in the PDT of non‐aggressive BCCs. Methods Ninety‐eight histologically verified non‐aggressive BCCs met the inclusion criter…

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface mo…

Tekoälyn perusteita ja sovelluksia

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning val…

Laskennallinen tiede - tieteen kolmas menetelmä : tilannekatsaus 2011

Hyperspectral imaging system in the delineation of Ill-defined basal cell carcinomas : a pilot study

Background Basal cell carcinoma (BCC) is the most common skin cancer in the Caucasian population. Eighty per cent of BCCs are located on the head and neck area. Clinically ill‐defined BCCs often represent histologically aggressive subtypes, and they can have subtle subclinical extensions leading to recurrence and the need for re‐excisions. Objectives The aim of this pilot study was to test the feasibility of a hyperspectral imaging system (HIS) in vivo in delineating the preoperatively lateral margins of ill‐defined BCCs on the head and neck area. Methods Ill‐defined BCCs were assessed clinically with a dermatoscope, photographed and imaged with HIS. This was followed by surgical procedures…

Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network

Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

CHOOSING OF OPTIMAL REFERENCE SAMPLES FOR BOREAL LAKE CHLOROPHYLL A CONCENTRATION MODELING USING AERIAL HYPERSPECTRAL DATA

Abstract. Optical remote sensing has potential to overcome the limitations of point estimations of lake water quality by providing spatial and temporal information. In open ocean waters the optical properties are dominated by phytoplankton density, while the relationship between color and the constituents is more complicated in inland waters varying regionally and seasonally. Concerning the difficulties relating to comprehensive modeling of complex inland and coastal waters, the alternative approach is considered in this paper: the raw digital numbers (DN) recorded using aerial remote hyperspectral sensing are used without corrections and derived by means of regression modeling to predict C…

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed

Minimal learning machine in anomaly detection from hyperspectral images

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data

Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this…

Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV

Hyperspectral imaging based precise fertilization is challenge in the northern Europe, because of the cloud conditions. In this paper we will introduce schemes for the biomass and nitrogen content estimations from hyperspectral images. In this research we used the Fabry-Perot interferometer based hypespectral imager that enables hyperspectral imaging from lightweight UAVs. During the summers 2011 and 2012 imaging and flight campaigns were carried out on the Finnish test field. Estimation mehtod uses features from linear and non-linear unmixing and vegetation indices. The results showed that the concept of small hyperspectral imager, UAV and data analysis is ready to operational use.

Noise reduction in asteroid imaging using a miniaturized spectral imager

In October 2024, European Space Agency’s Hera mission will be launched, targeting the binary asteroid Didymos. Hera will host the Juventas and Milani CubeSats, the first CubeSats to orbit close to a small celestial body performing scientific and technological operations. The primary scientific payload of the Milani CubeSat is the SWIR, NIR, and VIS imaging spectrometer ASPECT. The Milani mission objectives include mapping the global composition and the characterization of the binary asteroid surface. Onboard data processing and evaluation steps will be applied due to the limited data budget for the downlink to Earth and to perform the technological demonstration of a novel semi-autonomous h…

Tree species recognition in species rich area using UAV-borne hyperspectral imagery and stereo-photogrammetric point cloud

Abstract. Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with the original uncalibrated imagery. Alternative estimators were tested for predicting tree…

Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set. nonPeerReviewed

Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications

Abstract. The unmanned airborne system (UAS) remote sensing using lightweight multi- and hyperspectral imaging sensors offer new possibilities for the environmental monitoring applications. Based on the accurate measurements of the way in which the object reflect and emit energy, wide range of affecting variables can be monitored. Condition for reliable applications is reliable and accurate input data. In many applications, installation of geometric and radiometric reference targets in the object area is challenging, for instance, in forest or water areas. On the other hand, UASs are often operated in very poor conditions, under clouds or under variable cloud cover. Our objective is to deve…

Biomass estimator for NIR image with a few additional spectral band images taken from light UAS

A novel way to produce biomass estimation will offer possibilities for precision farming. Fertilizer prediction maps can be made based on accurate biomass estimation generated by a novel biomass estimator. By using this knowledge, a variable rate amount of fertilizers can be applied during the growing season. The innovation consists of light UAS, a high spatial resolution camera, and VTT's novel spectral camera. A few properly selected spectral wavelengths with NIR images and point clouds extracted by automatic image matching have been used in the estimation. The spectral wavelengths were chosen from green, red, and NIR channels.

Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near…

Research literature clustering using diffusion maps

We apply the knowledge discovery process to the mapping of current topics in a particular field of science. We are interested in how articles form clusters and what are the contents of the found clusters. A framework involving web scraping, keyword extraction, dimensionality reduction and clustering using the diffusion map algorithm is presented. We use publicly available information about articles in high-impact journals. The method should be of use to practitioners or scientists who want to overview recent research in a field of science. As a case study, we map the topics in data mining literature in the year 2011. peerReviewed

DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification

Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process for which such regulatory limitations limit the potential for new breakthroughs and collaborations. However, generating medically accurate synthetic data can alleviate privacy issues and potentially augment deep learning pipelines. This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity. We offer 320,000 synthetic (DeepFake) X-ray images from training with 5,556 real images. W…

Hyperspectral imaging of asteroids using an FPI-based sensor

The compositions of asteroids are of interest for the planetary sciences, mining, and planetary defense. The main method for evaluating these compositions is reflectance spectroscopy. Spectroscopic measurements performed from Earth can not resolve how different materials are distributed on the asteroids, making flyby-- and rendezvous missions necessary for obtaining detailed information. Using the CubeSat platform could reduce the costs of these missions, but it also sets constraints on the payload mass and volume. One small and light instrument capable of producing spatially resolved spectral data is a hyperspectral imager based on the Fabry-Perot interferometer. We propose a method of cal…

FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pé…

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Made available in DSpace on 2018-12-11T17:11:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-03-01 Suomen Akatemia Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees repr…

Detecting explosive substances by the IR spectrography

Fast and safe detection methods of explosive substances are needed both before and after actualized explosions. This article presents an experiment of the detection of three selected explosives by the ATR FTIR spectrometer and by three different IR hyperspectral imaging devices. The IR spectrometers give accurate analyzing results, whereas hyperspectral imagers can detect and analyze desired samples without touching the unidentified target at all. In the controlled explosion experiment TNT, dynamite and PENO were at first analyzed as pure substances with the ATR FTIR spectrometer and with VNIR, SWIR and MWIR cameras. After three controlled explosions also the residues of TNT, dynamite and P…

Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery

For accurate removal of malignant skin tumors, it is crucial to assure the complete removal of the lesions. In the case of certain ill-defined tumors, it is clinically challenging to see the true borders of the tumor. In this paper, we introduce several computationally efficient approaches based on spectral imaging to guide clinicians in delineating tumor borders. First, we present algorithms that can be used effectively with simulated skin reflectance data. By using simulated data, we gain detailed information about the sensitivity of the different approaches and how variables defined by algorithms act in the skin model. Second, we demonstrate the performance of the algorithms with spectra…

Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

The challenges of analysing blood stains with hyperspectral imaging

Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results s…

Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

Funding Information: Funding: This research was funded by Academy of Finland ICT 2023 Smart‐HSI—“Smart hyper‐ spectral imaging solutions for new era in Earth and planetary observations” (Decision no. 335612), by the European Agricultural Fund for Rural Development: Europe investing in rural areas, Pohjois‐ Savon Ely‐keskus (Grant no. 145346) and by the European Regional Development Fund for “Cyber‐ Grass I—Introduction to remote sensing and artificial intelligence assisted silage production” pro‐ ject (ID 20302863) in European Union Interreg Botnia‐Atlantica programme. This research was car‐ ried out in affiliation with the Academy of Finland Flagship “Forest‐Human‐Machine Interplay— Buildi…

Assessment of microalgae species, biomass, and distribution from spectral images using a convolution neural network

AbstractEffective monitoring of microalgae growth is crucial for environmental observation, while the applications of this monitoring could also be expanded to commercial and research-focused microalgae cultivation. Currently, the distinctive optical properties of different microalgae groups are targeted for monitoring. Since different microalgae can grow together, their spectral signals are mixed with ambient properties, making estimations of species biomasses a challenging task. In this study, we cultured five different microalgae and monitored their growth with a mobile spectral imager in three separate experiments. We trained and validated a one-dimensional convolution neural network by…

Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data

Osteoarthritis (OA) is the most common form of joint disease in the world. The diagnosis of OA is currently made by human experts and suffers from subjectivity, but recently new promising detection algorithms have been developed. We validated the current state-of-the-art KL-classifying neural network model for knee OA using knee X-rays taken from postmenopausal women suffering from knee pain attributable to OA. The performance of the model on the clinical data was considerably lower compared to the previous results on population-based test data. This suggests that the performance of the current grading methods is not yet adequate to be applied in clinical settings. The present results also …

Minimal learning machine in hyperspectral imaging classification

A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum is classified pixel-by-pixel. In some of the real-time applications, the amount of the HS image data causes performance challenges. Those issues relate to the platforms (e.g. drones) payload restrictions, the issues of the available energy and to the complexity of the machine learning models. In this study, we introduce the minimal learning machine (MLM) as a computationally cheap training and classification machine learning method for the hyperspectral imaging classificatio…

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…

HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER

Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral imag…

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasi…

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…

Optimization of spodumene identification by statistical approach for laser-induced breakdown spectroscopy data of lithium pegmatite ores

Mapping with laser-induced breakdown spectroscopy (LIBS) can offer more than just the spatial distribution of elements: the rich spectral information also enables mineral recognition. In the present study, statistical approaches were used for the recognition of the spodumene from lithium pegmatite ores. A broad spectral range (280–820 nm) with multiple lines was first used to establish the methods based on vertex component analysis (VCA) and K-means and DBSCAN clusterings. However, with a view to potential on-site applications, the dimensions of the datasets must be reduced in order to accomplish fast analysis. Therefore, the capability of the methods in mineral identification was tested wi…

Convolutional neural networks in skin cancer detection using spatial and spectral domain

Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed

PRACTICAL APPROACH FOR HYPERSPECTRAL IMAGE PROCESSING IN PYTHON

Abstract. Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.

UAV-based hyperspectral monitoring of small freshwater area

Recent development in compact, lightweight hyperspectral imagers have enabled UAV-based remote sensing with reasonable costs. We used small hyperspectral imager based on Fabry-Perot interferometer for monitoring small freshwater area in southern Finland. In this study we shortly describe the utilized technology and the field studies performed. We explain processing pipeline for gathered spectral data and introduce target detection-based algorithm for estimating levels of algae, aquatic chlorophyll and turbidity in freshwater. Certain challenges we faced are pointed out.

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…