High-resolution remote sensing image classification using OpenStreetMap data

doi:10.13474/j.cnki.11-2246.2019.0221

Abstract

Abstract: To address the difficulties of labeling samples for high-resolution remote sensing images, an automatic method of labeling samples employing OpenStreetMap (OSM) data is proposed in this paper. Different from the existing approaches, the proposed method adopts spatial features to supplement the shortcomings of using only spectral features. Firstly, samples are labeled using the category and position information of OSM data. As OSM data may contain errors, cluster analysis is utilized to refine the derived samples. Besides, to exploit the abundant spatial information provided by high resolution remote sensing images, morphological profiles are used to describe the structural features of the images. The spatial features as well as spectral features are combined for classification. Experiments show that the proposed method can significantly avoid the manpower and material resources required for labeling samples artificially. Meanwhile, the derived samples and the spectral-spatial features both contribute to the classification accuracy.

Key words: sample labeling, OpenStreetMap, morphological profiles, cluster analysis, high-resolution remote sensing images

CLC Number:

P237

HAO Huaixu, WAN Taili, LUO Nianxue. High-resolution remote sensing image classification using OpenStreetMap data[J]. 测绘通报, 2019, 0(7): 69-72,126.

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