Multi-platform optical remote sensing dataset for target detection

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. 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.本研究首创了一套用于目标检测研究的高分辨率多平台（地面、机载与星载）遥感基准数据集。该数据集包含地面高光谱成像仪（THI）、机载高光谱传感器（AVIRIS-NG）以及星载多光谱传感器（Sentinel-2）采集的影像数据。研究人员将五种不同材质与色彩的人工设计目标布设至多种地表背景之上。此外，研究人员还通过光谱辐射计采集了目标的原位反射光谱，以作为光谱参考源。针对机载与星载影像，研究人员首先剔除了未校准波段与噪声波段，并采用基于辐射传输方法的光谱超立方体快速视线路大气分析（FLAASH，Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes）模型完成大气校正。随后将原位目标反射光谱进行光谱重采样，以匹配机载与星载影像的光谱特性。此外，研究人员借助全球定位系统（GPS）获取的目标地面坐标，在机载与星载影像中为每个目标划定了感兴趣区域（ROI）。本文提供了一套天地一体化的目标检测数据集，包含目标地面坐标对应的感兴趣区域、点位与像元级原位目标参考光谱，以及预处理完成的机载与星载影像，可直接投入使用。本实验采集的数据集旨在评估多尺度多平台观测场景下人工材质目标检测的应用潜力。该数据集可用于遥感领域各类战略与民用应用场景下的目标检测算法测试与验证，是极具价值的研究资源。

搜集汇总

背景与挑战

背景概述

该数据集是一个多平台光学遥感基准数据集，专为目标检测研究设计，包含地面、机载和星载传感器获取的影像数据，并集成了工程目标的参考光谱和位置信息。数据集经过预处理，如大气校正和光谱匹配，适用于多尺度目标检测算法的测试与验证，具有战略和民用遥感应用价值。