Abstract
Abstract. 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.
Highlights
In recent years, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information; the finer-scale built-up areas can be detected and more accurate boundary can be obtained
Builtup areas are compound geographical objects consisting of different types of man-made structures, and the textural and structural features in HRSI become clearer as well as more complex due to the increased spatial resolution, which makes it more challenging to accurately detect built-up areas in HRSI than in medium- and low-resolution images
The local feature points based on Gabor filters were used to locate the buildings followed by spatial voting to achieve the detection of urban areas (Sirmacek and Ünsalan, 2010)
Summary
Built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information; the finer-scale built-up areas can be detected and more accurate boundary can be obtained. The local feature points based on Gabor filters were used to locate the buildings followed by spatial voting to achieve the detection of urban areas (Sirmacek and Ünsalan, 2010). To better locate built-up areas, corner points and straight lines were employed to indicate the existence of building features and the spatial voting algorithm was used for modeling their spatial distribution (Tao et al, 2013; Chen et al, 2016; Ning and Lin, 2017). We introduce multi-resolution wavelet transform and local spatial statistics to model the spatial patterns of builtup areas in HRSIs. By multi-resolution wavelet decomposition, the high-frequency subbands representing the detail information were extracted and fused to construct a saliency map, which was further modulated and enhanced by Getis-Ord statistic. Based on the derived saliency map, an adaptive threshold technique is utilized to achieve the detection of built-up areas
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
