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How to derive contour of buildings from VHR images is the essential problem for automatic building extraction in urban area. To solve this problem, OSM data is introduced to offer vector contour information of buildings which is hard to get from VHR images. First, we import OSM data into database. The line string data of OSM with tags of building, amenity, office etc. are selected and combined into completed contours; Second, the accuracy of contours of buildings is confirmed by comparing with the real buildings in Google Earth; Third, maximum likelihood classification is conducted with the confirmed building contours, and the result demonstrates that the proposed approach is effective and accurate. The approach offers a new way for automatic interpretation of VHR images.

Significant radiometric differences and weak grayscale correlations exist between optical and SAR images. As a result, there are severe spectral and spatial distortions in the fused images. We propose a fusion method of optical and SAR remote sensing images that couples the gain injection method and the guided filter. The proposed method is based on the fusion framework of generalized intensity-hue-saturation non-subsampled contourlet transform, and the gain injection is used for the low-frequency coefficient fusion to reduce the spectral distortion. Then, the divergence is used as the activity measure operator to calculate the initial weight template for the high-frequency coefficients. The guided filter is used to optimize the edge details of the initial weight template. The fused high-frequency coefficients are obtained by weighted average. Through comparison experiments with existing fusion methods, the results show that the proposed method has the best quality of fusion and the proposed method has the best performance. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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