The automatic extraction of building outlines from high-resolution images is an important and challenging task. Convolutional neural networks have shown excellent results compared with traditional building extraction methods because of their ability to extract high-level abstract features from images. However, it is difficult to fully utilize the multiple features of current building extraction methods; consequently, the resulting building boundaries are irregular. To overcome these limitations, we propose a method for extracting buildings from high-resolution images using a multifeature convolutional neural network (MFCNN) and morphological filtering. Our method consists of two steps. First, the MFCNN, which consists of residual connected unit (RCU), dilated perception unit (DPU), and pyramid aggregation unit (PAU), is used to achieve pixel-level segmentation of the buildings. Second, morphological filtering is used to optimize the building boundaries, improve the boundary regularity, and obtain refined building boundaries. The Massachusetts and Inria datasets are selected for experimental analysis. Under the same experimental conditions, the extraction results achieved with the proposed MFCNN are compared with the results of other deep learning models that have been commonly used in recent years: FCN-8s, SegNet, and U-Net. The results on both datasets reveal that the proposed model improves the F1-score by 3.31%-5.99%, increases the overall accuracy (OA) by 1.85%-3.07%, and increases the intersection over union (IOU) by 3.47%-8.82%. These findings illustrate that the proposed method is effective at extracting buildings from complex scenes.

Edge optimization of semantic segmentation results is a challenging issue in remote sensing image processing. This paper proposes a semantic segmentation model guided by a block-in-block edge detection network named BIBED-Seg. This is a two-stage semantic segmentation model, where edges are extracted first and then segmented. We do two key works: The first work is edge detection, and we present BIBED, a block-in-block edge detection network, to extract the accurate boundary features. Here, the edge detection of multi-scale feature fusion is first realized by creating the block-in-block residual network structure and devising the multi-level loss function. Second, we add the channel and spatial attention module into the residual structure to improve high-resolution remote sensing images' boundary positioning and detection accuracy by focusing on their channel and spatial dimensions. Finally, we evaluate our method on ISPRS Potsdam and Vaihingen data sets and obtain ODS F-measure of 0.6671 and 0.7432, higher than other excellent edge detection methods. The second work is two-stage segmentation. First, the proposed BIBED is individually pre-trained, and subsequently, the pre-trained model is introduced into the entire segmentation network to extract boundary features. In the second segmentation stage, the edge detection network is used to constrain semantic segmentation results by loss cycles and feature bootstrapping. Our best model obtains the OA of 90.2%, 87.7%, and 81.5%, the IOU of 76.0%, 69.6%, and 61.3% on the ISPRS and WHDLD datasets, respectively.

Scientific and appropriate visualizations increase the effectiveness and readability of disaster information. However, existing fusion visualization methods for disaster scenes have some deficiencies, such as the low efficiency of scene visualization and difficulties with disaster information recognition and sharing. In this paper, a fusion visualization method for disaster information, based on self-explanatory symbols and photorealistic scene cooperation, was proposed. The self-explanatory symbol and photorealistic scene cooperation method, the construction of spatial semantic rules, and fusion visualization with spatial semantic constraints were discussed in detail. Finally, a debris flow disaster was selected for experimental analysis. The experimental results show that the proposed method can effectively realize the fusion visualization of disaster information, effectively express disaster information, maintain high-efficiency visualization, and provide decision-making information support to users involved in the disaster process.

Extracting building instances from remote sensing images has various applications. However, existing instance segmentation methods have difficulty maintaining regular and angular building boundaries, corrupting the mask quality. This letter proposes a boundary-optimized instance segmentation method based on the direction field to cope with the low boundary quality. The proposed method develops a DF-Mask head to improve the mask quality of the primary instance segmentation network (Mask R-CNN) through boundary optimization. Within the DF-Mask head, first, a gated directional context-aware module (GDCAM) improves the mask features through the directional context and a designed gating mechanism. Then a direction field rectification module (DFRM) predicts the direction field and iteratively rectifies the mask. To the best of our knowledge, this is the first time that direction field rectification has been developed to the instance segmentation method and applied to building instance extraction. Experimental results on the Vaihingen and Massachusetts datasets suggest that our method outperforms the eight state-of-the-art methods. Compared with Mask R-CNN, our method improves APs by 4.7% and 2.2% on the two datasets, respectively.

Remote sensing image scene classification is a significant direction in the field of remote sensing research. The method based on deep learning has become the most popular method in recent years because it can realize the automatic feature extraction and classification of remote sensing images. The deep learning requires a large number of samples for training and consumes large computing resources, and the data augmentation can alleviate the problem of insufficient samples. The image manipulation is one of the most commonly methods, but it may cause the loss of key information in the image. In this paper, we proposed an improved supervised data augmentation method based on Class Activation Map (CAM) and image manipulation, and then used this method to augment the high-resolution remote sensing images of NWPU dataset. We utilized three CNNs networks to count the classification accuracy of the remote sensing images. The experimental results show that the proposed method increases the accuracy of scene classification by more than 0.4%. The CAM-based methods provide a new technical support for the scene classification of remote sensing images based on deep learning.