Change detection using hyperspectral images is important in surveillance and reconnaissance operations. The process involves two images: one reference and one test. Many algorithms such as chronochrome (CC) and covariance equalization (CE) were proposed in the past. In this paper, we will present a new nonlinear change detection framework for hyperspectral images. The idea was motivated by the band rationing concept. First, image segmentation is applied to the reference image. For each segmented subimage in the reference image, the bands with the most and least variations are found. Then new images are formed by dividing the two bands. Similarly, the new band ratioed images are formed in the test images. Second, we propose to apply CC or CE to generate residual images. Finally, anomaly detection algorithms are applied to detect changes. Actual hyperspectral images have been used in our studies. Receiver operating characteristics (ROC) curves were used to compare the various options. Results showed that this approach can achieve excellent detection performance.
Bulent Ayhan, Chiman Kwan, and Jin Zhou, "A new nonlinear change detection approach based on band ratioing," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064410 (Presented at SPIE Defense + Security: April 19, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2303648.
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