Affiliations: [a] Henan Vocational College of Water Conservancy and Environment, Zhengzhou, Hunan 450008, China | [b] Hohai University, School of Earth Science and Engineering, Nanjing 213100, China
Correspondence:
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Corresponding author: Yuzhen Wang, Henan Vocational College of Water Conservancy and Environment, Zhengzhou, Hunan 450008, China. E-mail: 1037234058@qq.com.
Abstract: As an important way to obtain ground information, high resolution remote sensing images have been widely used in many fields, such as urban planning, base map updating, cadastre investigation, and precision agriculture. Because the application of remote sensing images on different occasions puts forward different requirements for remote sensing image processing, the important link in image processing–image classification, becomes even more important. This paper begins with image segmentation, combines algorithm of patch purification, region growing and superpixel segmentation, to implement semi-supervised classification using a small number of labeled sample points to obtain higher-accuracy classification results.
Keywords: High resolution remote sensing imagery, image segmentation
