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Discriminative Multiple-Instance Hyperspectral Subpixel Target Characterization
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AI
DOI：10.1109/TGRS.2022.3140798.png)
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Abstract
En 中文
Subpixel target detection in hyperspectral imagery is challenging since subpixel targets are smaller in size than the resolution of a single pixel and accurate pixel-level labels on subpixel targets are often unavailable. In particular, this article addresses the problem of learning a prime prototype target signature from imprecisely labeled highly mixed hyperspectral data. Two algorithms, multiple-instance subpixel adaptive cosine estimator (MI-SPACE) and multiple-instance subpixel spectral matched filter (MI-SPSMF), based on multiple-instance learning framework are presented. The proposed methods aim to learn a discriminative prime target signature by maximizing the posterior detection statistics of subpixel hyperspectral targets for the correspondingly proposed subpixel adaptive cosine estimator (SPACE) and subpixel spectral matched filter (SPSMF) detectors, which are also developed in this article. Experimental results demonstrate the effectiveness of the proposed methods on both simulated and real-field hyperspectral subpixel target detection tasks.
Keywords:
Hyperspectral imaging
Detectors
Object detection
Matched filters
Global Positioning System
Training
Task analysis
Adaptive cosine estimator
hyperspectral
imprecise labels
multiple-instance learning
spectral matched filter
subpixel target characterization
Journal
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8.6
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2.1W
Citations:
10.7W