Extraction of buildings in remote sensing imagery based on multi-level segmentation and classification hierarchical model and feature space optimization
Tao DANG1,2, Qi SONG1, Yong LIU2, Anjian XU1, Bo XU1, Honggang ZHANG1
1. Xi’an Information Technique Institute of Surveying and Mapping, Xi’an, 710054, China; 2. College of Earth and Enviromental Sciences, Lanzhou University, Lanzhou 730000, China
In view of the problems of scale effect, spectral diversity and classification feature optimization in the extraction of urban objects information from high spatial resolution remote sensing images,the authors, based on the object-based image analysis method and combined with data mining and machine learning,propose a multi-level segmentation and classification hierarchical model and its feature space optimization method for building extraction. First, according to the multi-scale characteristics of remote sensing information, a hierarchical relationship is set up for the difference of features of ground objects, and then a hierarchical structure based on information segmentation and classification is established based on the characteristics of spectral diversity to define the subtypes of ground objects. After that, the proposed Relief F-PSO combination feature selection method is used. Finally,on the basis of multiscale segmentation and feature optimization, the water surface distribution is obtained based on the random forest model, and finally the building information is extracted by the J48 decision tree algorithm. Experimental results show that the method can utilize a small number of image feature attributes to get high-precision building extraction results.
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Tao DANG, Qi SONG, Yong LIU, Anjian XU, Bo XU, Honggang ZHANG. Extraction of buildings in remote sensing imagery based on multi-level segmentation and classification hierarchical model and feature space optimization. Remote Sensing for Land & Resources, 2019, 31(3): 111-122.
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