Hyperspectral Camouflaged Identification Driven by Spatial-Spectral
Characteristics
LIU En-qin1, 2, HUANG Wei3, XU Yong3, YANG Man2, GAO Bing2, MO Ding-ru4
1. Key Laboratory of Smart Earth, Beijing 100029, China
2. College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
3. People's Liberation Army Unit 61287, Chengdu 610036, China
4. College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
Abstract：To address the issues of incomplete contour and fragmented spatial information caused by the sole use of spectral features in hyperspectral remote sensing identification of camouflage, this study proposes a method for identifying camouflage in hyperspectral remote sensing by integrating spatial and spectral features. An imaging spectrometer was employed to capture close-range hyperspectral imagery spanning 400~900 nm spectral range with 1 nm resolution under a grassland background. Spectral features (first derivative, second derivative, spectral indices, etc.) and texture features (mean, entropy, second moment, etc.) were extracted, and the differences between camouflaged objects and the grassland background were analyzed. The Mahalanobis distance method was used to screen out sensitive parameters conducive to camouflage identification, and then multiple band combination strategies were proposed, resulting in five datasets being constructed. Three methods, namely, multi-layer perceptron neural network (MLP), support vector machine (SVM), and spectral angle (SAM), were used to identify camouflage. The results show that: (1) Among the spectral feature variables, the red band, near-infrared, “red edge”, and narrow-band spectral indices (CR1, ARI1, and ARI2) are very beneficial for the identification of camouflage. Among the texture feature variables, mean and contrast are sensitive bands for the identification of camouflage. (2) Compared with the identification results using only spectral or texture features, the dataset integrating spatial-spectral features has better integrity and higher accuracy in identifying camouflage. The "red edge" feature alone failed to identify camouflage, but when combined with other bands, it could identify camouflage. (3) Among the five datasets, dataset 4, composed of four bands (red, near-infrared, mean, and contrast), has the highest accuracy in identifying camouflage, with a producer's accuracy of 99.85% and a user's accuracy of 99.34%. This band combination strategy can be extended to the identification of camouflage in multispectral remote sensing images. (4) Among the three identification methods, SVM performs the best overall and can effectively identify camouflage, while SAM performs poorly. The research results can be extended to the identification of new color schemes and camouflage patterns, and the selected sensitive bands and effective identification features can serve as the basis for feature extraction and target identification in hyperspectral remote sensing images from unmanned aerial vehicles and satellites.
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