Abstract
Aiming at the difficulties in change detection caused by the complexity of high-resolution remote sensing images that exist in varied ecological environments and artificial objects, in order to overcome the limitations in traditional pixel-oriented change detection methods and improve the detection precision, an innovative object-oriented change detection approach based on multiscale fusion is proposed. This approach introduced the classical color texture segmentation algorithm J-segmentation (JSEG) to change detection and achieved the multiscale feature extraction and comparison of objects based on the sequence of J-images produced in JSEG. By comprehensively using the geometry, spectrum, and texture features of objects, and proposing two different multiscale fusing strategies, respectively, based on Dempster/Shafer evidence theory and weighted data fusion, the algorithm further improves the divisibility between changed and unchanged areas, thereby establishing an integrated framework of object-oriented change detection based on multiscale fusion. Experiments were performed on high-resolution airborne and SPOT 5 remote sensing images. Compared with different object-oriented and pixel-oriented detection methods, results of the experiments verified the validity and reliability of the proposed approach.
Highlights
As one of the most popular research topics in current application of remote sensing, change detection for multitemporal remote sensing images is essentially a process of determining the information of geophysical changes using remote sensing images of the same area at different temporals.[1]
change vector analysis (CVA)-EM algorithm uses the difference image generated by CVA method and introduces EM algorithm to estimate the relevant parameters of Gaussian model, which obviously yields a higher detection precision
The following conclusions can be drawn: 1. The detection framework proposed in the paper is effective and reliable in urban change detection in high-resolution remote sensing images
Summary
As one of the most popular research topics in current application of remote sensing, change detection for multitemporal remote sensing images is essentially a process of determining the information of geophysical changes using remote sensing images of the same area at different temporals.[1]. As a major application field, urban change detection has played an important role in city planning and management. For moderate- and low-resolution remote sensing images, various effective change detection methods have been proposed by scholars, and most of these methods can achieve reliable results by comparing each pixel in images.[2,3,4,5,6,7,8]. The phenomenon of “the same object with different spectrums” is much more serious, and the phenomenon of “the same spectrum with different objects” still exists, so that it is difficult to differentiate changed areas from unchanged areas.[10] Second, urban landscapes include various ecological environments and complex artificial objects
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