| Summary: | Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data.However,it is a non-trivial task to classify HSIaccurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel,semi-supervised,superpixel-levelclassification method foran HSIwas proposed based on a graph and discrete potential (SSC-GDP). The key ideaof the proposed schemeis the construction of the weighted connectivity graph and the division of the weighted graph.Based on the superpixel segmentation,aweighted connectivity graph is constructed usingthe weighted connection between a superpixelandits spatial neighbors. The generatedgraphisthen dividedinto different communities/subgraphsby using a discrete potential and theimproved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graphrepresents a class inthe HSI. The local connection strategy, together with thelinear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatialmethod, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.
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