Abstract
High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good performance in the absence of massive annotated samples. MAPs have been proven to have a strong ability for extracting detailed characterizations of buildings with multiple attributes and scales. So far, a great deal of attention has been paid to this application. Nevertheless, the constraints of rational selection of attribute scales and evidence conflicts between attributes should be overcome, so as to establish reliable unsupervised detection models. To this end, this research proposes a joint optimization and fusion building detection method for MAPs. In the pre-processing step, the set of candidate building objects are extracted by image segmentation and a set of discriminant rules. Second, the differential profiles of MAPs are screened by using a genetic algorithm and a cross-probability adaptive selection strategy is proposed; on this basis, an unsupervised decision fusion framework is established by constructing a novel statistics-space building index (SSBI). Finally, the automated detection of buildings is realized. We show that the proposed method is significantly better than the state-of-the-art methods on HRRS images with different groups of different regions and different sensors, and overall accuracy (OA) of our proposed method is more than 91.9%.
Highlights
With the rapid development of earth observation technology, building detection based on high-resolution remote sensing (HRRS) images has been one of the research hotspots in the field of remote sensing [1]
(3) Buildings should be a type of geographical objects with closed contours, and how to automatically convert potential building pixels extracted based on morphological attribute profiles (MAPs) into object-level building detection results is another challenge to be tackled. In response to these challenges, we propose an automatic building detection method from High-resolution remote sensing (HRRS) images based on the joint optimization and decision fusion of MAPs
(2) Based on ACGA-differential attribute profiles (DAPs) and image segmentation results, we propose an unsupervised decision fusion framework, which bridges the gap between potential building pixels and object-level building detection results
Summary
With the rapid development of earth observation technology, building detection based on high-resolution remote sensing (HRRS) images has been one of the research hotspots in the field of remote sensing [1]. Remote sensing images have the characteristics of wide coverage, strong timeliness, and a large amount of obtainable information, which are helpful for cognition and interpretation of geographical targets. Buildings occupy an important position in the area of human activities. The spatial characteristics and distribution of urban buildings represent important basic data for urban construction management, such as national survey monitoring, urban and rural planning management, real estate management [2], etc. The study of automatic high-precision detection of buildings on remote sensing images is significant for further developing remote sensing image informa- 4.0/).
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