RESUMEN
Maintaining rich biodiversity and being a habitat and resource for humans, tropical forests are one of the most important global biomes. These forest ecosystems have been experiencing a host of unregulated anthropogenic activities including illegal tourism, and shifting cultivation. The presence of human-habitats in the restricted zones of forest ecosystems is a direct indicator of the human activities that may accelerate deterioration of forest quality by area and tree species composition. Remote sensing data have been extensively used for mapping forest types, and biophysical characterization at various spatial scales. Several remote sensing datasets from multispectral, hyperspectral and LIDAR sensors are available for developing and validating a host of methodologies for remote sensing application in forestry. However, quantifying the quality of forest stands and detecting potential threats from the sporadic and small-scale human activities requires sub-pixel level remote sensing data analysis methods such as, spectral mixture modelling. Generally, most of the studies employ pixel-level supervised learning-based analysis techniques to detect infrastructure and settlements. However, if the settlements are smaller than the ground sampling distance and are under the canopy, pixel-based techniques are not suitable. Reinvigorated with progressive availability of hyperspectral imagery, spectral mixture modelling based sub-pixel image analysis is gaining prominence in the contemporary remote sensing application development. However, there is a paucity of high-resolution hyperspectral imagery and associated ground truth spectral measurements for assessing various methodological approaches on studies related to anthropogenic activities and forest disturbance. Most of the studies have relied upon simulating and synthesising the hyperspectral imagery and its associated ground truth spectra for implementation of methods and algorithms. This article presents a distinct dataset of high-resolution hyperspectral imagery and associated ground truth spectra of various vegetable crops acquired over a tropical forest ecosystem. The dataset is valuable for research on developing new discrimination models of forest and cultivated vegetation, classification methods, spectral matching analysis techniques, and sub-pixel target detection methods.
RESUMEN
Recent developments in the miniaturization of hyperspectral imaging sensors have given rise to the increased use of hyperspectral imagery as the primary data for evaluating spectral unmixing algorithms in applications such as industrial quality control, agriculture, mineral mapping, military, etc. This article presents an ultra-high-resolution hyperspectral imagery dataset for undertaking benchmark studies on spectral unmixing. A terrestrial hyperspectral imager (THI) is used for imaging the target scene with the camera sensor pointing horizontally towards the target scene. The datasets are acquired at various spatial resolutions ranging from 1 mm to 2 cm. The targeted scene contains several paper-based panels, each size of 2 cm x 2 cm and filled with different colours and proportions, glued to a black background board that maintains a distinguishable distance between one another. In addition to the hyperspectral imagery data acquisitions, reference spectral signatures of the candidate mixture materials are obtained by in-situ hyperspectral reflectance measurements using a spectroradiometer. The hyperspectral image acquisition and the in-situ spectral signatures of the target scene are collected under natural illumination conditions. The proposed datasets are designed for undertaking proof-of-the-concept (PoC) studies in spectral unmixing. The datasets are also valuable for evaluating the performance of different statistical and machine learning algorithms for target detection, classification, and sub-pixel classification algorithms.
RESUMEN
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.