作者：
Wan, YutingZhong, YanfeiMa, AilongWuhan Univ
State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Wuhan Univ
Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Hubei Peoples R China
clustering of remote sensing imagery is a tough task due to the particular and complex structure of remote sensing images and the shortage of known information. In this paper, we propose a fullyautomatic spectral-spa...
详细信息
clustering of remote sensing imagery is a tough task due to the particular and complex structure of remote sensing images and the shortage of known information. In this paper, we propose a fullyautomatic spectral-spatial fuzzy clustering method using an adaptive multiobjective memetic algorithm (AMOMA) for multispectral remote sensing imagery. This approach is made up of two automatic layers: an automatic determination layer and an automaticclustering layer. The first layer seeks the optimal number of clusters through a self-adaptive differential evolution algorithm. The second layer then takes advantage of the AMOMA for spectral-spatial clustering using the optimal number of clusters. The knee point from the Pareto front is then selected through the angle-based method in every generation, and we then compare the knee points between generations to output the final optimal solution. The effectiveness of the proposed method is verified by the experimental results obtained with three remote sensing data sets.
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