Building outlines have many applications. However, owing to the diversity of buildings and the complexity of the surrounding environment, automatic extraction of building outlines from remote sensing data remains challenging. This paper presents a novel approach for extracting building outlines from digital surface models (DSM) and orthophotographs. The DSM provides initial contour lines, while the orthophotograph indicates where vegetation is obstructing the building outline. The approach introduces two key algorithms: Distance-Constrained Clustering (DCC), to cluster contour lines, and Gradient-based Optimal Contour Selection (G-OCS), to select building outlines. Vegetation information is used to recover obstructed building outlines and improve outline accuracy and completeness. Experimental results, using the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark dataset, demonstrate the method’s performance (quality metric: 85.0% for individual regions, 73.6% for individual objects, and 99.1% for objects >50 m). Validation using a dataset from Shandong Province (China) confirmed the method’s robustness and applicability for complex urban environments. The approach effectively handles challenges such as interference from vegetation and irregular building structures, outperforming techniques such as WHUZ, CNN/8F+, and HD-Net. This novel method automates building outline extraction and provides useful building information, with applications in urban planning, disaster management, and smart city development.

Remote sensing change detection (RSCD) is a key tool for environmental monitoring and resource management, playing a significant role in monitoring dynamic surface changes. In practical applications, RSCD often requires high precision and efficient detection methods. However, traditional methods tend to involve high technical complexity and a large number of parameters, and are susceptible to interference from complex background noise, leading to poor performance in detecting change areas. To address these issues, this paper proposes a lightweight remote sensing change detection network, LMG-Net. The model uses a lightweight encoder and incorporates a Hierarchical Transformer Module (HTF) to suppress background noise and minimize parameter increase, effectively extracting multi-level global features. Additionally, the paper introduces a Multi-dimensional Cooperative Attention Guidance (MAG) mechanism, further enhancing the ability to detect boundary changes. The model has only 3.29M parameters and a computational load of 3.89G, demonstrating its high applicability, particularly for real-time applications in resource-constrained environments. Experimental results show that LMG-Net achieves state-of-the-art F1 scores and IoU values on the WHU-CD, SYSU-CD, and LEVIR-CD+ datasets: (94.79%, 90.09%), (82.29%, 69.90%), and (84.30%, 71.14%).

Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre- and post-temporal images are used for training and testing, respectively, and the training set is purified by using two-classifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs fine-grained scene classification. The change rules and post-processing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method’s efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

As the basic and essential component of most object detectors, a feature pyramid network (FPN) can effectively extract multiscale features to recognize objects at different scales. Nevertheless, due to the cross-scale fusion and upsampling operations in FPN, current detectors still suffer from information loss and feature misalignment. To alleviate these problems, a flow-guided upsampling module (FGUM) and a multifeature attention module (MFAM) are proposed to improve the FPN. Specifically, FGUM uses a novel flow warp in the upsampling operation to align features, resulting in better cross-scale fusion. At the same time, the MFAM module fully considers the integrity of multiscale features and reduces the aliasing effect by optimizing the weight of each level of features. To verify the effectiveness of the improved FPN, it is applied to three state-of-the-art models for experiments, and all of them achieve better detection accuracy compared to the original FPN.

Scene classification plays a significant role in the field of remote sensing (RS). Recently, the rapid development of convolutional neural networks (CNNs) has enabled a vital breakthrough in high-resolution RS image scene classification. However, complex backgrounds and small objects in high-resolution RS images pose challenges to the application of CNNs. To this end, a novel multibranch fusion attention network (MBFANet) is proposed to improve the feature extraction ability and generalization performance of models. Specifically, a multibranch fusion attention module (MBFAM) is designed by adaptively fusing two parallel submodules, namely, efficient pooling channel attention module (EPCAM) and efficient convolution coordinate attention module (ECCAM), which helps the model focus on more key cues in images that are difficult to classify. The ablation experiments in RS scene classification datasets demonstrate the effectiveness of our methods. In addition, MBFANet achieves competitive results on three benchmark datasets.

Remote sensing (RS) scene classification is challenging due to changes in the scale and direction of scenes within a category. Bilinear pooling method can extract higher-order and spatial orderless information and has been shown to achieve impressive performance on various visual tasks. However, bilinear pooled features are high dimensional, which makes them impractical for subsequent processing, especially for the convolutional neural network (CNN) models with more channels in the final convolutional layer. To alleviate this shortcoming, an improved bilinear pooling method is proposed to build the compact bilinear CNN model in this work. Specifically, a joint pooling method is proposed to reduce the high-dimensional bilinear features, and it can be embedded in a bilinear CNN architecture for end-to-end optimization. Through the experimental evaluation of three real RS scene image data sets, it is proved that the improved bilinear pooling method can obtain features with higher discriminative power than the bilinear pooling method but with lower dimensionality. In addition, it also reduces the running time of model training.

Deep convolutional networks have been the most competitive method in remote sensing scene classification. Due to the diversity and complexity of scene content, remote sensing scene classification still remains a challenging task. Recently, the second-order pooling method has attracted more interest because it can learn higher-order information and enhance the nonlinear modeling ability of the networks. However, how to effectively learn second-order features and establish the discriminative feature representation of holistic images is still an open question. In this letter, we propose a first and second-order information fusion network (FSoI-Net) that can learn the first-order and second-order features at the same time, and construct the final feature representation by fusing the two types of features. Specifically, a self-attention-based second-order pooling (SaSoP) method based on covariance matrix is proposed to extract second-order features, and a fusion loss function is developed to jointly train the model and construct the final feature representation for the classification decision. The proposed network has been thoroughly evaluated on three real remote sensing scene datasets and achieved better performance than the counterparts.