Semantic understanding based on multi-feature kernel sparse representation and decision rules for mangrove growth

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With the rapid development of remote sensing technology, using remote sensing technology is an important means to monitor the dynamic change of land cover and ecology. In view of the complexity of mangrove ecological monitoring in Dongzhaigang, Hainan Province of China, we propose a semantic understanding method of mangrove remote sensing image by combining a multi-feature kernel sparse classifier with a decision rule model in this paper. First, on the basis of multi-feature extraction, we take into account the spatial context relations of the samples and introduce the kernel function into the sparse representation classifier, a multi-feature kernel sparse representation classifier can be constructed to classify cover types of mangroves and their surrounding objects. Second, in view of growth conditions of mangrove area, we put forward a semantic understanding method of mangrove remote sensing image based on decision rules and divide mangrove and non-mangrove areas by combining classification results of the multi-feature kernel sparse representation classifier. We make a divisibility analysis based on the extracted features of spatial and spectral domains. Then select the best split attribute based on the maximum information gain criterion, to generate a semantic tree and extract semantic rules. Finally, we work on the semantic understanding of mangrove areas in line with decision rules and further divide mangrove areas into two categories: excellent growth and poor growth. Experimental results show that the proposed method can effectively identify mangrove areas and make decisions on mangrove growth.