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

Sudhanshu Shekhar Jha,Rama Rao Nidamanuri

doi:10.3390/rs12132145

Sudhanshu Shekhar Jha, Rama Rao Nidamanuri

Open Access

https://doi.org/10.3390/rs12132145

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Abstract

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.

Highlights

Technological advancements in remote sensing systems have led to the availability of compact and high-resolution imaging sensors deployable on the ground, airborne, and space-borne platforms
As a result thatspectral reflective signatures of different materials are distinct in the optical range of the electromagnetic spectrum (EM), remote sensing data have been used for land surface characterization from local to a global level
The first component was approached by quantifying the magnitude and patterns of variation of probability of detection (PD) with the three levels of platforms considered

Summary

Introduction

Technological advancements in remote sensing systems have led to the availability of compact and high-resolution imaging sensors deployable on the ground, airborne, and space-borne platforms. Building upon the broader application domain of hyperspectral remote sensing, various organizations have developed spectral libraries of reference spectral signatures for thousands of natural and human-made materials [1,2,3]. Target detection is one of the general approaches of remote sensing, which has a broader application perspective. Detecting targets—specific material objects (natural or engineered) of interest, with a sparse spatial distribution in remote sensing imagery has been an active area of research. Various mapping and surveillance requirements in defense, mineralogy, and precision agriculture can be addressed quickly from a target detection perspective in remote sensing imagery. From a target detection perspective, high-resolution multispectral imagery has been used for identifying common land use objects such as buildings, roads, vehicles, and ships [7,8]. Hyperspectral imagery offers appropriate baseline spectral data with finer spectral bandwidth required for typical target detection problems

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