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Proposed AMMF and Spectral Constraint Strategy
Experimental Results and Discussion
Abstract: Change detection (CD) for multitemporal remote sensing images can find change trends and reveal the development patterns. However, the typical methods based on algebraic ... View more
Change detection (CD) for multitemporal remote sensing images can find change trends and reveal the development patterns. However, the typical methods based on algebraic operation, image transformation, or segmentation may not yield satisfactory results due to the spectral variability and noise complexity. In a sense, it can be considered that multitemporal images are composed of unchanged and changed regions, as well as noise. In the light of this, a stepwise subtraction method for CD is proposed, based on auto-updating multitemporal matrix factorization (AMMF) and spectral constraint, to remove the unchanged regions and noise from the original images step by step. The unchanged regions are first identified by AMMF, during which the distribution and subspace information of each temporal image are regularized to encode the spatio-temporal correlation. Then, mean shift smoothness is adopted as a spectral constraint so as to remove the noise. In this way, the changed regions have been highlighted so that the change map can be obtained by a postsegmentation method. Experiments have been conducted on three multitemporal data sets, including images from Quick Bird, aerial, and GF-1, indicating that the proposed method is effective and robust, which is superior to some state-of-the-art methods.
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Change detection (CD) for remote sensing images is an efficient way to identify the differences between multitemporal images, and subsequently to explore the development trends or discover the change patterns. In recent years, it has drawn significant attention due to its extensive applications in vegetation and land coverage analysis, urban development, and environment and damage monitoring [1], [2].
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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© Copyright 2023 IEEE - All rights reserved.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
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