Authors:
Mingxue Zheng
1
and
Huayi Wu
2
Affiliations:
1
Wuhan University and Delft University of Technology, China
;
2
Wuhan University, China
Keyword(s):
Classification, Locally Adaptive Dictionary, Collaborative Representation, High-Resolution Remote Sensing Image.
Abstract:
Sparse representation is widely applied in the field of remote sensing image classification, but sparsity-based methods are time-consuming. Unlike sparse representation, collaborative representation could improve the efficiency, accuracy, and precision of image classification algorithms. Thus, we propose a high-resolution remote sensing image classification method using collaborative representation with a locally adaptive dictionary. The proposed method includes two steps. First, we use a similarity measurement technique to separately pick out the most similar images for each test image from the total training image samples. In this step, a one-step sub-dictionary is constructed for every test image. Second, we extract the most frequent elements from all one-step sub-dictionaries of a given class. In the step, a unique two-step sub-dictionary, that is, a locally adaptive dictionary is acquired for every class. The test image samples are individually represented over the locally adapt
ive dictionaries of all classes. Extensive experiments (OA (%) =83.33, Kappa (%) =81.35) show that our proposed method yields competitive classification results with greater efficiency than other compared methods.
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