Building Extraction from High Resolution Remote Sensing Imagery with Multi-feature and Multi-scale

doi:10.13474/j.cnki.11-2246.2017.0378

Abstract

Abstract: Buildings vary in different shapes and sizes in high resolution remote sensing imageries, and the phenomenon that large within-class spectral variations and between-class spectral confusions also exist. In this paper, a method based on spectral feature, shape feature, texture feature and multilevel segmentation is proposed. Firstly, the original extraction is carried out by calculating the MBI, threshold segmentation and shape features. Then, object-oriented analysis is used for multi-scale segmentation and texture feature is used for building recognition in single scale. Finally, multi-scale fusion is used for the ultimate extraction. The presented method is evaluated with an image of Okinawa, Japan. The experiments show that the proposed building extraction algorithm can provide satisfactory precision ratio with a high level of recall ratio.

Key words: high resolution remote sensing imagery, building extraction, multi-feature, multi-scale segmentation, multi-scale fusion

CLC Number:

P237

LIN Yuzhun, ZHANG Baoming, XU Junfeng, HOU Kai, ZHOU Xun. Building Extraction from High Resolution Remote Sensing Imagery with Multi-feature and Multi-scale[J]. 测绘通报, 2017, 0(12): 53-57.

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