BUILT-UP AREA DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGES USING MULTI-SCALE WAVELET TRANSFORM AND LOCAL SPATIAL STATISTICS

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

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Chen, Y. / Zhang, Y. / Gao, J. / et al: BUILT-UP AREA DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGES USING MULTI-SCALE WAVELET TRANSFORM AND LOCAL SPATIAL STATISTICS. 2018. Copernicus Publications.

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