Search results for "Spektri"

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

2013

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…

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

2020

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…

Eksperimentālā fizika, 3. sējums: Optika

1929

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

2020

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…

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

2020

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…

Hyperspectral imaging of asteroids using an FPI-based sensor

2021

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…

Prazeodīma luminiscence oksifluorīdu stiklā un nātrija lantānfluorīdu saturošā oksifluorīdu stikla keramikā

2016

Ar retzemju elementiem aktivēti oksifluorīda stikli un stikla keramikas ir perspektīvi luminiscenti materiāli, kas pēc ķīmiskā sastāva ir vienādi, bet kuru optiskās īpašības atšķiras, jo retzemju elementu iebūvēšanās vietas stikla un stikla keramikas struktūrā atšķiras, līdz ar to arī luminiscences procesi stiklā un stikla keramikā ir dažādi. Darba gaitā sintezēti oksifluorīda stikli ar atšķirīgām prazeodīma koncentrācijām – 0,01 mol%, 0,1 mol% un 1 mol%, katras koncentrācijas paraugu pagatavojot līdzīgās temperatūrās. Iegūtie stiklu paraugi pēc atdzesēšanas tika atkārtoti karsēti dažādās temperatūrās, lai iegūtu NaLaF4 saturošus stikla keramikas paraugus. Gan stikla, gan stikla keramikas p…

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network

2020

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

MATLAB codes implementing the generalized cross-wavelet transform (GXWT) algorithm described in the paper "Analyzing multidimensional movement intera…

2021

MATLAB codes implementing the generalized cross-wavelet transform (GXWT) algorithm described in the paper "Analyzing multidimensional movement interaction with generalized cross-wavelet transform" (Toiviainen & Hartmann, 2021). Basic workflow for multivariate (time by channel) signals d1 and d2: [w1,f] = cwtensor(d1,FS,MINF,MAXF); [w2,f] = cwtensor(d2,FS,MINF,MAXF); [xs p1 p2] = getxwt(w1,w2); NOTE: cwtensor.m requires the Wavelet Toolbox for MATLAB.

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

2022

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…