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.

Land cover and land use (LULC) classification of very fine spatial resolution remote sensing images is a challenging task. Though the object-based convolutional neural network (OCNN) has proven to be an effective method for LULC classification, it still has some shortcomings. For example, it is difficult to achieve a well-segmented result with few parameter adjustments. Besides, the traditional convolutional neural network (CNN) is hard to make full use of the spectral information in the remote sensing images. To this end, we propose an improved LULC classification method. Specifically, an adaptive segmentation algorithm is used to automatically adjust segmentation parameters to achieve the best-segmented results. Due to the different areas and shapes in segmented units, a unique sample extraction method is proposed to better extract representative samples. For better classification, a new CNN model is also constructed to fully use spectral information. The proposed method has been validated on two real remote sensing images and achieved excellent classification performance.

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.

In this letter, CatBoost was first introduced and investigated for remote sensing (RS) image classification using diverse features. To improve the classification performance by fostering the effective and efficient spatial feature extraction, a new pseudo label features (PLFs) extraction method was proposed via multisize neighboring patches-based multiclustering. Experimental results on two hyperspectral and one PolSAR benchmarks showed that: 1) CatBoost is an advanced ensemble learning (EL) algorithm for classification of RS images using diverse features; 2) CatBoost has better capability of reducing the overfitting issue at large number of boosting iteration; and 3) proposed PLFs can result in compatible and even better classification results than using morphological profiles (MPs) and MPs with partial reconstruction (MPPR) spatial features.

An effective network structure is essential for the classification of satellite image time series (SITS). Deep learning models have been widely used for SITS classification and achieved impressive performance, especially the architectures based on self-attention. However, the lack of efficient and comprehensive attention to valuable bands and time series structure hinders the performance to some extent. To address this problem, an end-to-end attention-aware dynamic self-aggregation network (ADSN) is proposed for SITS classification in this work, which combines two main parts: spectral focusing and spectral–temporal feature learning. The core components of ADSN are the channel attention module and dynamic self-aggregation block. Specifically, informative bands in the SITS flowing through the channel attention module can adaptively get a high weight to increase their contributions, while the attentions of some low-efficiency bands are weakened. Besides, the dynamic self-aggregation block, which integrates multiscale dynamic convolution and improved multihead attention in parallel, can simultaneously capture long- and short-distance sequence structures and position relationships to better represent temporal information. Compared with random forest (RF) and seven deep learning algorithms, the proposed model effectively learns spectral and temporal features, and the experimental results confirm that ADSN has achieved superior classification accuracy and generalization ability on two SITS datasets with extremely unbalanced samples.

In active learning (AL)-based remote sensing (RS) image classification tasks, the acquisition of labeled data depends not only on the informativeness and representativeness measured in feature space but also on the spatial distributions and relations in an image plane. However, very few studies have investigated the advantages of integrating spatial constraints into the AL paradigm. Hence, under the basic assumption “instances that are difficult to classify are usually located around edges between different objects or land-cover types,” edge gradient information was integrated into the conventional AL paradigm using popular uncertainty and diversity measurements. The experimental results with two real hyperspectral images confirmed the advantages of the proposed edge gradient-based AL (EGAL) approach from the aspects of fast convergence and computationally efficient operation.

The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios’ classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C‐order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state‐of‐the‐art AL methods are evaluated, such as breaking ties, margin sampling, and multi‐class level uncertainty, with or without diversity criteria such as angle‐based diversity (ABD), clustering‐based diversity (CBD), and enhanced clustering‐based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones.

Scene classification from remote sensing images provides new possibilities for potential application of high spatial resolution imagery. How to efficiently implement scene recognition from high spatial resolution imagery remains a significant challenge in the remote sensing domain. Recently, convolutional neural networks (CNN) have attracted tremendous attention because of their excellent performance in different fields. However, most works focus on fully training a new deep CNN model for the target problems without considering the limited data and time-consuming issues. To alleviate the aforementioned drawbacks, some works have attempted to use the pretrained CNN models as feature extractors to build a feature representation of scene images for classification and achieved successful applications including remote sensing scene classification. However, existing works pay little attention to exploring the benefits of multilayer features for improving the scene classification in different aspects. As a matter of fact, the information hidden in different layers has great potential for improving feature discrimination capacity. Therefore, this paper presents a fusion strategy for integrating multilayer features of a pretrained CNN model for scene classification. Specifically, the pretrained CNN model is used as a feature extractor to extract deep features of different convolutional and fully connected layers; then, a multiscale improved Fisher kernel coding method is proposed to build a mid-level feature representation of convolutional deep features. Finally, the mid-level features extracted from convolutional layers and the features of fully connected layers are fused by a principal component analysis/spectral regression kernel discriminant analysis method for classification. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing data sets, and shows the competitive performance compared with fully trained CNN models, fine-tuning CNN models, and other related works.