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…
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…
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…
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
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…
HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM
Abstract. The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, w…
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…
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…
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…
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.
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.
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…
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.
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…
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…