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 to .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.

中文翻译：

---

Gudalur光谱目标检测（GST-D）：多平台遥感数据中的新基准数据集和工程材料目标检测

遥感图像中的目标检测，稀疏分布材料的制图，在国防安全和监视，矿产勘探，农业，环境监测等方面具有重要应用。检测概率和检索质量取决于传感器，平台各种参数的功能，目标-背景动态，目标的光谱对比度和大气影响。通常，在图像形成过程中假设线性的情况下，已经使用各种统计检测算法来实现遥感图像中的目标检测。有关图像采集几何形状，光谱特征及其在不同成像平台上的稳定性的知识对于设计光谱目标检测系统至关重要。我们进行了综合目标检测实验，以检测各种人造目标材料。作为这项工作的一部分，我们获取了一个称为“ Gudalur光谱目标检测（GST-D）”数据集的基准多平台高光谱和多光谱遥感数据集。将人造目标定位在不同的表面背景上，我们于2018年3月20日通过地面，空中和天基传感器获取了遥感数据。在基准数据集上采用了各种统计和子空间检测算法来检测目标，同时考虑了不同的来源参考目标光谱，背景以及整个平台的光谱连续性。我们针对不同情况下的检测算法和成像平台，使用接收器操作曲线（ROC）验证了检测结果。结果表明，对于算法和成像平台的某些组合，特定物质目标的一致检测率约为80％，误报率介于到之间。使用机载高光谱图像的参考目标光谱对卫星图像进行目标检测与卫星图像得出的参考光谱。基于地面的原位参考光谱可对机载或卫星图像进行定量检测。但是，地面高光谱图像还提供了机载和卫星图像中的等效目标检测，从而为快速获取参考目标光谱铺平了道路。