Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (P_cb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.

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%.