Multi-platform optical remote sensing dataset for target detection

Sudhanshu Shekhar Jha; Manohar Kumar; Rama Rao Nidamanuri

doi:10.1016/j.dib.2020.106362

Multi-platform optical remote sensing dataset for target detection

Data Brief. 2020 Oct 1:33:106362. doi: 10.1016/j.dib.2020.106362. eCollection 2020 Dec.

Authors

Sudhanshu Shekhar Jha¹, Manohar Kumar¹, Rama Rao Nidamanuri¹

Affiliation

¹ Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, Kerala, India.

Abstract

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.

Keywords: AVIRIS-NG; Engineered material detection; Field reflectance spectroscopy; Multi-platform remote sensing; Sub-pixel material detection; Target detection; Terrestrial hyperspectral imaging.