Next Article in Journal
High-Resolution Coherency Functionals for Improving the Velocity Analysis of Ground-Penetrating Radar Data
Next Article in Special Issue
Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection
Previous Article in Journal
Hydrometeor Distribution and Linear Depolarization Ratio in Thunderstorms
Open AccessArticle

Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, Kerala 695547, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2145; https://doi.org/10.3390/rs12132145
Received: 20 May 2020 / Revised: 10 June 2020 / Accepted: 18 June 2020 / Published: 3 July 2020
Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective. View Full-Text
Keywords: target detection; multi-platform imaging; spectral matching; terrestrial-hyperspectral imagery; automated image analysis; spectral library target detection; multi-platform imaging; spectral matching; terrestrial-hyperspectral imagery; automated image analysis; spectral library
Show Figures

Graphical abstract

MDPI and ACS Style

Jha, S.S.; Nidamanuri, R.R. Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data. Remote Sens. 2020, 12, 2145. https://doi.org/10.3390/rs12132145

AMA Style

Jha SS, Nidamanuri RR. Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data. Remote Sensing. 2020; 12(13):2145. https://doi.org/10.3390/rs12132145

Chicago/Turabian Style

Jha, Sudhanshu S.; Nidamanuri, Rama R. 2020. "Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data" Remote Sens. 12, no. 13: 2145. https://doi.org/10.3390/rs12132145

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop