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Multi-platform optical remote sensing dataset for target detection
Target detection in remote sensing has vital applications in mineral mapping, law enforcement, precision agriculture, strategic surveillance, etc. We present the acquisition of a first-of-its-kind high-resolution multi-platform (ground, airborne, and space-borne) remote sensing-based benchmark datas...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560709/ https://www.ncbi.nlm.nih.gov/pubmed/33088874 http://dx.doi.org/10.1016/j.dib.2020.106362 |
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author | Jha, Sudhanshu Shekhar Kumar, Manohar Nidamanuri, Rama Rao |
author_facet | Jha, Sudhanshu Shekhar Kumar, Manohar Nidamanuri, Rama Rao |
author_sort | Jha, Sudhanshu Shekhar |
collection | PubMed |
description | Target detection in remote sensing has vital applications in mineral mapping, law enforcement, precision agriculture, strategic surveillance, etc. We present the acquisition of a first-of-its-kind high-resolution multi-platform (ground, airborne, and space-borne) remote sensing-based benchmark dataset for target detection studies. The dataset includes imagery acquired from terrestrial hyperspectral imager (THI), airborne hyperspectral sensor (AVIRIS-NG), and space-borne multi-spectral (Sentinel-2) sensor on 20th March 2018. Five engineered targets of different materials and colours were placed on different surface backgrounds. Besides, in-situ reflectance spectra of the targets were also acquired using a spectroradiometer for serving as a spectral reference source. The airborne and space-borne imagery were processed to remove un-calibrated/noisy bands and were atmospherically corrected using a radiative transfer method based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. The in-situ target reflectance spectra were resampled to spectrally match with airborne and space-borne imagery. Further, a target region of interest (ROI) was designated for each of the targets in both airborne and space-borne imagery using the known ground position of targets using a GPS device. This article provides a ground to space integrated target detection dataset, including ground positions ROI of the targets, point, and pixel-based in-situ target reference spectra, and the processed airborne and space-borne imagery to make the dataset ready for use. The data acquired in this experiment is an attempt to assess the potential of engineered material target detection in a multi-scale multi-platform view setup. The dataset is a valuable resource for testing and validation of target detection algorithms from various strategic and civilian application perspectives of remote sensing. |
format | Online Article Text |
id | pubmed-7560709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75607092020-10-20 Multi-platform optical remote sensing dataset for target detection Jha, Sudhanshu Shekhar Kumar, Manohar Nidamanuri, Rama Rao Data Brief Data Article Target detection in remote sensing has vital applications in mineral mapping, law enforcement, precision agriculture, strategic surveillance, etc. We present the acquisition of a first-of-its-kind high-resolution multi-platform (ground, airborne, and space-borne) remote sensing-based benchmark dataset for target detection studies. The dataset includes imagery acquired from terrestrial hyperspectral imager (THI), airborne hyperspectral sensor (AVIRIS-NG), and space-borne multi-spectral (Sentinel-2) sensor on 20th March 2018. Five engineered targets of different materials and colours were placed on different surface backgrounds. Besides, in-situ reflectance spectra of the targets were also acquired using a spectroradiometer for serving as a spectral reference source. The airborne and space-borne imagery were processed to remove un-calibrated/noisy bands and were atmospherically corrected using a radiative transfer method based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. The in-situ target reflectance spectra were resampled to spectrally match with airborne and space-borne imagery. Further, a target region of interest (ROI) was designated for each of the targets in both airborne and space-borne imagery using the known ground position of targets using a GPS device. This article provides a ground to space integrated target detection dataset, including ground positions ROI of the targets, point, and pixel-based in-situ target reference spectra, and the processed airborne and space-borne imagery to make the dataset ready for use. The data acquired in this experiment is an attempt to assess the potential of engineered material target detection in a multi-scale multi-platform view setup. The dataset is a valuable resource for testing and validation of target detection algorithms from various strategic and civilian application perspectives of remote sensing. Elsevier 2020-10-01 /pmc/articles/PMC7560709/ /pubmed/33088874 http://dx.doi.org/10.1016/j.dib.2020.106362 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Jha, Sudhanshu Shekhar Kumar, Manohar Nidamanuri, Rama Rao Multi-platform optical remote sensing dataset for target detection |
title | Multi-platform optical remote sensing dataset for target detection |
title_full | Multi-platform optical remote sensing dataset for target detection |
title_fullStr | Multi-platform optical remote sensing dataset for target detection |
title_full_unstemmed | Multi-platform optical remote sensing dataset for target detection |
title_short | Multi-platform optical remote sensing dataset for target detection |
title_sort | multi-platform optical remote sensing dataset for target detection |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560709/ https://www.ncbi.nlm.nih.gov/pubmed/33088874 http://dx.doi.org/10.1016/j.dib.2020.106362 |
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