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.

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m–3.76 m, which can achieve high-precision estimation of building height.

Extracting building heights from single-view remote sensing images greatly enhances the application of remote sensing data. While methods for extracting building height from single-view shadow images have been widely studied, it remains a challenging task. The main reasons are as follows: (1) The traditional method for extracting shadow information exhibits low accuracy. (2) The use of only shadow information to extract building height results in limited application scenarios. To solve the above problems, this paper introduces building side and shadow information to complement each other, and proposes a building height extraction method from high-resolution single-view remote sensing images using shadow and side information. Firstly, we propose the RMU-Net method, which utilizes multi-scale features for the extraction of shadow and side information. This method aims to address issues related to pixel detail loss and imprecise edge segmentation, which result from significant scale differences within segmentation targets. Additionally, we employ the area threshold method to optimize the segmentation results, specifically to tackle small stray patches and holes, enhancing the overall integrity and accuracy of shadow and side information extraction. Secondly, we propose a method for building height extraction that integrates shadow and side information based on an enhanced proportional coefficient model. The accuracy of measuring building side and shadow lengths is improved by incorporating the fishing net method, informed by our analysis of the geometric relationships among buildings. Finally, we establish a dataset containing building shadow and side information from remote sensing images, and select multiple areas for experimental analysis. The results demonstrate a shadow extraction accuracy of 91.03% and a side extraction accuracy of 90.29%. Additionally, the average absolute error (MAE) for building height extraction is 1.22, while the average root mean square error (RMSE) is 1.21. Furthermore, the proposed method's validity and scalability are affirmed through experimental analyses of applicability and anti-interference performance in extensive areas.

Despite significant advancements in salient object detection (SOD) in optical remote sensing images (ORSIs), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning (DL) approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the edge feature adaptive balancing and adjusting (EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the global distributed affinity learning (GDAL) module to model global context. It captures global context by generating an affinity map from the encoder’s final layer, ensuring effective modeling of global patterns. In addition, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state-of-the-art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method. The codes of our method are available at https://github.com/little1bold/LBA-MCNet .

Deep learning methods are widely used in building information extraction from remote sensing images (RSIs). However, this task still faces great challenges. Firstly, it is difficult to perform accurate boundary localization due to the complex contextual relationship of the building boundary area; Secondly, the heterogeneity caused by different materials on building tops, as well as environmental factors such as climate and vegetation, complicates the extraction of top information; Lastly, given the complexity of the RSIs, the accuracy and generalization of the existing method still need to be further enhanced. This study introduces the Boundary Enhancement and Multi-scale Refinement Fusion Network (BEMRF-Net) to overcome these challenges. The Boundary-aware Self-Attention Module (BSA) is initially proposed to refine the network's boundary detection capabilities. It applies the distance transform algorithm across both channel and spatial dimensions, reducing boundary fluctuation and enhancing the accuracy of building boundary extraction. Subsequently, we present the Multi-Scale Refined Fusion Module (MSRF) to resolve discontinuities within buildings caused by rooftop obstructions. This module effectively merges high and low-level features, overcoming information gaps through the strategic integration of Multi-scale data. To demonstrate the efficacy of our methodology, we conducted comprehensive experiments and analyses using the WHU Building Dataset and the Inria Aerial Image Labeling Dataset, both known for their complexity. Our method surpassed 16 contemporary state-of-the-art (SOTA) techniques, achieving IoU scores of 91.15% and 79.52% for each dataset, respectively, marking the highest accuracy levels reported. Further, in-depth discussions on efficiency, generalizability, and ablation studies emphasized our method's robustness and adaptability.

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.