Identification method of urban villages with improved composite dictionary considering multi-resolution features

doi:10.13474/j.cnki.11-2246.2023.0102

Abstract

Abstract: Urban village is a special type of urban settlement, and accurate and effective monitoring and identification of urban village is conducive to achieving coordinated development of urban and rural areas and optimizing the ecological environment. Existing object-oriented urban village identification methods usually require a large amount of sample data, resulting in high training cost and low data update efficiency. In order to solve the problems, a composite dictionary urban village identification method considering multi-resolution characteristics is proposed. Firstly, we use dense grid sampling to extract SIFT global features, and fuse them with multi-resolution color vector angular histogram features to form a visual dictionary. Then we use the image representation as a visual word frequency histogram. Finally, the random forest classifier is classified to realize the identification of urban villages at scene scale. The overall accuracy of the proposed method reaches 90.08% and the Kappa coefficient reaches 80.16%. It is 8.99%, 3.51%, 4.78% and 2.28% higher than that of SURF, SIFT, VGG16 and ResNet50, respectively.

Key words: identification of urban villages, high-resolution remote sensing images, composite dictionary, multi-resolution color features, histogram feature fusion

CLC Number:

P237

XING Ruoyun, RAN Shuhao, GAO Xianjun, YANG Yuanwei, FANG Jun. Identification method of urban villages with improved composite dictionary considering multi-resolution features[J]. Bulletin of Surveying and Mapping, 2023, 0(4): 41-48,48.

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