Abstract
Unsupervised change detection(CD) from remotely sensed images is a fundamental challenge when the ground truth for supervised learning is not easily available. Inspired by the visual attention mechanism and multi-level sensation capacity of human vision, we proposed a novel multi-scale analysis framework based on multi-scale visual saliency coarse-to-fine fusion (MVSF) for unsupervised CD in this paper. As a preface of MVSF, we generalized the connotations of scale as four classes in the field of remote sensing (RS) covering the RS process from imaging to image processing, including intrinsic scale, observation scale, analysis scale and modeling scale. In MVSF, superpixels were considered as the primitives for analysing the difference image(DI) obtained by the change vector analysis method. Then, multi-scale saliency maps at the superpixel level were generated according to the global contrast of each superpixel. Finally, a weighted fusion strategy was designed to incorporate multi-scale saliency at a pixel level. The fusion weight for the pixel at each scale is adaptively obtained by considering the heterogeneity of the superpixel it belongs to and the spectral distance between the pixel and the superpixel. The experimental study was conducted on three bi-temporal remotely sensed image pairs, and the effectiveness of the proposed MVSF was verified qualitatively and quantitatively. The results suggest that it is not entirely true that finer scale brings better CD result, and fusing multi-scale superpixel based saliency at a pixel level obtained a higher F1 score in the three experiments. MVSF is capable of maintaining the detailed changed areas while resisting image noise in the final change map. Analysis of the scale factors in MVSF implied that the performance of MVSF is not sensitive to the manually selected scales in the MVSF framework.
Highlights
As the fast development of imaging technology gives rise to easy access to image data, dealing with bi-temporal or multi-temporal images has been getting great concerns.Change detection (CD) is to detect changes from multiple images covering the same scene at different time phrases
We proposed a novel unsupervised change detection method based on multi-scale visual saliency coarse-to-fine fusion (MVSF), aiming to develop an effective visual saliency based multi-scale analysis framework for unsupervised change detection
False negative (FN) denotes the number of pixels which are classified into the unchanged regions but changed in the reference change map, while false positive (FP) represents the number of pixels which are classified into the changed regions but unchanged in the reference change map
Summary
As the fast development of imaging technology gives rise to easy access to image data, dealing with bi-temporal or multi-temporal images has been getting great concerns. Two main branches of the scholar community have been working on this problem, one is the computer vision (CV) and the other is remote sensing (RS) The former analyses the changes among multiple natural images or video frames to carry out further applications, such as object tracking, visual surveillance, and smart environments, etc. We would like to take a fresh look at the CD procedure We consider it as a visual process when people conduct CD artificially from remotely sensed images. Detailed changes can be found and delineated if people put attention on the changed areas of various sizes We consider this sophisticated visual procedure to be attributed to the visual attention mechanism and multi-level sensation capacity of the human visual system.
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