A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network

Qu, Jiahui; Xu, Zhangchun; Dong, Wenqian; Xiao, Song; Li, Yunsong; Du, Qian

doi:10.1109/tnnls.2022.3189049

Cited by 15 publications

(2 citation statements)

References 52 publications

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“…The field of homogeneous change detection has seen significant advancements with the application of deep-learning techniques. These innovations have led to more sophisticated and efficient methods for analyzing changes in both optical remote sensing images and hyperspectral images, as well as SAR images [11][12][13][14][15][16][17][18][19][20][21][22][23]. For instance, Wu et al [14] proposed a spatial-temporal association-enhanced mobile-friendly vision transformer specifically designed for the change detection of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [16] contributed by introducing a knowledge distillation-based lightweight change detection method for high-resolution remote sensing imagery, aimed at facilitating on-board processing. Qu et al [17] developed a cycle-refined multidecision joint alignment network, tailored for unsupervised domain adaptive hyperspectral change detection, highlighting the continuous refinement in the field. Zhang et al [18] presented a novel approach combining a convolution and attention mixer specifically for SAR image change detection, showcasing the ongoing evolution of methodologies.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Li,

Ma,

Zhang

et al. 2024

Remote Sensing

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Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

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