Tensor Regression and Image Fusion-Based Change Detection Using Hyperspectral and Multispectral Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 9794 - 9802
• Main Authors: Zhan, Tianming, Sun, Yanwen, Tang, Yongsheng, Xu, Yang, Wu, Zebin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Change detection; Computer vision; Detection; Dictionaries; hyperspectral images (HSIs); Hyperspectral imaging; Image classification; Image fusion; Image processing; Image resolution; Mathematical analysis; Methods; multispectral images (MSIs); Regression; Regression analysis; Remote sensing; Resolution; Spatial discrimination; Spatial resolution; Spectral resolution; Task analysis; tensor regression; Tensors
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3115345

Change detection is a popular topic in remote sensing that is generally constrained to two remote sensing images captured at two different times. However, the optimal type of remote sensing image for change detection tasks has not yet been determined. The use of only hyperspectral images (HSIs) with low spatial resolution or multispectral images (MSIs) with low spectral resolution cannot obtain satisfactory change detection results. In this article, we propose the fusion of simultaneously captured low spatial resolution HSIs and low spectral resolution MSIs with the use of a tensor regression-based method to detect change regions from the fused images at two different time points. In this method, nonlocal couple tensor CP decomposition is initially applied to fuse the HSIs and MSIs. A difference image is then obtained by subtracting the fused images at two different time points. Thereafter, the tensors are extracted from the difference image and the tensor regression-based method is used to classify the difference image and detect the final change results. Experimental results from three real datasets suggest that the proposed method substantially outperforms the existing state-of-the-art change detection methods as well as any change detection methods using single-source images.