BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

Abstract

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

“…As shown in Figure 2, compared with the ordinary convolution layer, the atrous convolution can effectively expand the receptive field of feature extraction, retaining the feature size and reducing the loss of spatial information of features without increasing parameters. It has been proved to be an effective feature extraction approach [50]. We use multiple parallel atrous convolution branches in the Resblock module of the network to obtain the global characteristics of the road surface and road centerline in remote sensing images.…”

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

“…To validate the generalization of the proposed method on imagery with a different spatial resolution, we conducted experiments on the Massachusetts dataset to compare the performance of our and related works. BRRNet [62] designed a prediction module based on the encoder-decoder structure with a residual refinement module for accurate boundary extraction. The results showed that our method outperformed the classical semantic segmentation methods and the most recent building extraction methods, as shown in Table 3.…”

A Deep Cross-Modality Hashing Network for SAR and Optical Remote Sensing Images Retrieval

“…Specifically, the same class of remote sensing images might have a diverse appearance [29]. Numerous studies have shown superior performances to the traditional handcrafted features in CBRSIR, based on the great success of the convolutional neural network (CNN) in representing high-level visual features of images [31][32][33][34][35][36][37][38]. Similarly, numerous studies are underway to increase retrieval accuracy by extracting more discriminative features of the images, considering the small and intricate targets contained in the remote sensing images.…”