Abstract
In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.
Highlights
Change detection is referred to observing and processing the same area of multitemporal images at different time
Fuzzy c-means (FCM) algorithms, which can get the degree of uncertainty of feature data belonging to each class and expresses the intermediate property of their memberships, have been widely used in the change
The proposed method includes three main parts: (1) the uncertainty and certainty regions are determined by combining the threshold of magnitudes of difference image (DI) (MDI) with the one of spectral angle information (SAI); (2) construction of mass function based on fuzzy c-means (FCM) algorithm and D-S evidence combination for the MDI and SAI in uncertainty regions; and (3) parameter optimization based on conflict index
Summary
Change detection is referred to observing and processing the same area of multitemporal images at different time. In the past few years, many pattern recognition algorithms, such as support vector machine [4] and deep learning neural networks [11], have been applied for the change detection of remotely sensed images In these algorithms, fuzzy c-means (FCM) algorithms, which can get the degree of uncertainty of feature data belonging to each class and expresses the intermediate property of their memberships, have been widely used in the change. The fuzzy exponent of FCM objective function is adaptively determined by the total conflict degree between the MDI and spectral angle information (SAI) of uncertainty region in bitemporal images.
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