Assessment of target detection limits in hyperspectral data

Abstract

Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classiffed. The goal of this paper is to determine the target size to spatial resolution ratio for successful classiffcation of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. Commonly used classiffcation algorithms, e.g. Matched Filtering, Adaptive Cosine Estimator are used to determine the detection limit. Fixed false alarm rates are employed to find and analyze certain regions where false alarms usually occur first. A combination of 18 targets and 12 backgrounds is analyzed for three VNIR and two SWIR data sets of the same area. ABSTRACTHyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classified. The goal of this paper is to determine the target size to spatial resolution ratio for successful classification of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouflage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. ...

Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

“…A discussion of varying behavior of different mixtures in original data can be found in Gross et al 1 Especially the difference between Camouflage 1 and Camouflage 2 in SWIR data is prominent and can be explained by …”

“…An RGB color composite of the selected target and background areas of the aisa data is depicted in Figure 1. Other targets and backgrounds from Gross et al 1 were neglected due to the computational complexity and run time of the bandselection algorithms. Synthetic target and background mixtures are generated using linear mixtures of each with varying ratios.…”

“…In 2015 we published an approach to approximate the subpixel target detection limits in hyperspectral data in Gross et al 1 Multiple commonly used classifiers were analyzed, e.g. Spectral Angular Mapper (SAM), 2 Matched Filter (MF), 3 Adaptive Coherence Estimator (ACE) 3 and Orthogonal Subspace Projection (OSP).…”

Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

“…Remote sensing technologies are already widely used in military context [2,8,18]. Flying platforms and their corresponding sensor-(systems) experienced a fast development during the last decade [10,13,16], mainly driven by the establishment of drone technology, active 3D point measurements and the increasing number of operating sensor channels.…”

“…Passive sensors with the highest number of imaging bands are the so-called hyperspectral (HS) sensors or spectrometers. Their airborne installation, image acquisition and analysis is already state of the art [8,11,12,17] and the data offers ground resolution in a centimeter scale. Since hyperspectral sensors are becoming smaller, their usage on drones is just upcoming [1,4].…”