Abstract
Abstract. DLR’s Earth Sensing Imaging Spectrometer (DESIS) is mounted on the International Space Station (ISS). DESIS records data in the spectral range from 400 to 1000 nm with a spectral and spatial resolution of 2.55 nm and 30 m respectively. The high spectral resolution enables in detecting a target object distinctly in remotely sensed imagery which has many useful applications in different fields of surveillance and monitoring. In present work two different case studies have been carried out that use DESIS data for target detection. In the first case study brick kilns are detected in DESIS data using Adaptive Coherence Estimator (ACE) algorithm. In the second case study Photovoltaic (PV) panels are considered as target object and linear spectral unmixing is employed to distinctly detect them in the image. From experimental results it is observed that the first target which were sparsely located in the image is detected very precisely with F1 score value of 0.97. The accuracy of the output of PV panel detection is observed to be more than 98%. Both the case studies show the potential of DESIS data in target detection which is a very important application of hyperspectral remote sensing.
Highlights
Hyperspectral image with its rich spectral information has found many applications in various fields (Paul et al.; 20215), such as astronomy, agriculture (Datt et al, 2003), mineralogy (Hörig et al, 2001), military (Eismann et al, 2009), and in particular, target detection (Manolakis et al, 2014; Frontera-Pons et al, 2017, Cavalli et al, 2013)
Results of target detection using DLR’s Earth Sensing Imaging Spectrometer (DESIS) data for two different target objects are discussed in following subsections
It is observed in this figure that Brick kilns are detected correctly and this shows the case of true positive detection
Summary
Hyperspectral image with its rich spectral information has found many applications in various fields (Paul et al.; 20215), such as astronomy, agriculture (Datt et al, 2003), mineralogy (Hörig et al, 2001), military (Eismann et al, 2009), and in particular, target detection (Manolakis et al, 2014; Frontera-Pons et al, 2017, Cavalli et al, 2013). The concept of target detection is to find out whether a pixel is occupied (fully or partially) by the target material or it belongs to the background (Bitar et al, 2020). The huge spectral dimension of hyperspectral image is generally reduced to obtain better classification accuracy in certain applications (Paul and Chaki, 2021a; Paul and Bhoumik, 2021; Paul et al, 2021a). Target detection is basically a binary classification that labels each pixel in the image either as a target or background
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