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• Improving Coastline Edge Detail: EDS module enhances edge fitting. • Bridging Semantic Gap: CSDS and AFM collaboration enhances semantic information. • Outperforms with mIoU: CSAFNet achieves remarkable 96.72% mIoU value. The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 1 1 The dataset was initially labeled as "sea-land segmentation data" by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction. [ABSTRACT FROM AUTHOR]

Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification methods based on convolutional neural networks (CNN) have been extremely successful. However, the limitations of CNN itself make it difficult to acquire global information. The traditional Vision Transformer can effectively capture long-distance dependencies for acquiring global information, but it is computationally intensive. In addition, each class of scene in remote sensing images has a large quantity of the similar background or foreground features. To effectively leverage those similar features and reduce the computation, a highly efficient lightweight vision transformer (HELViT) is proposed. HELViT is a hybrid model combining CNN and Transformer and consists of the Convolution and Attention Block (CAB), the Convolution and Token Merging Block (CTMB). Specifically, in CAB module, the embedding layer in the original Vision Transformer is replaced with a modified MBConv (MBConv ∗ ), and the Fast Multi-Head Self Attention (F-MHSA) is used to change the quadratic complexity of the self-attention mechanism to linear. To further decreasing the model's computational cost, CTMB employs the adaptive token merging (ATOME) to fuse some related foreground or background features. The experimental results on the UCM, AID and NWPU datasets show that the proposed model displays better results in terms of accuracy and efficiency than the state-of-the-art remote sensing scene classification methods. On the most challenging NWPU dataset, HELViT achieves the highest accuracy of 94.64%/96.84% with 4.6G GMACs for 10%/20% training samples, respectively. [ABSTRACT FROM AUTHOR]

Deep learning has achieved remarkable success in the semantic segmentation of remote sensing images (RSIs).In the domain of semantic segmentation, where classification and localization tasks need to be performed simultaneously, it is crucial to consider both global and local spatial relationships in RSIs. This is especially important for the recognition of ground objects that have a slim and elongated appearance. However, existing methods for land use semantic segmentation lack an effective mechanism to coordinate and address these two aspects, resulting in limitations on the recognition of slim targets and the continuity of land object identification. Here, a specific attention-based network called PaANet is developed for semantic segmentation. Our proposed framework builds upon the Swin transformer by incorporating two key modules: the position-aware attention (PaA) module and the pyramid pooling expectation-maximization (PPEM) module. These modules provide significant improvements in recognition accuracy and the continuity of ground object recognition while preserving structural classification details. Furthermore, we propose a multiresolution data augmentation method that utilizes scale-related information to guide the encoder. This approach leads to improved performance and generalization ability for the model. In experiments, the mIoU of our approach for the BLU and GID datasets is 2.37% and 3.94% higher than that of the baseline model, respectively. Our results also show significant superior to those of other methods regarding the continuity of ground object recognition. [ABSTRACT FROM AUTHOR]

Remote sensing images (RSI) of urban regions often exhibit shadows cast by buildings and other objects, which may result in imprecise analysis and interpretation. Therefore, shadow detection plays a significant role in RSI scene understanding. Current approaches have paid little attention to the presence of pseudo shadows and their confounding effects on detection results. We tackle these questions in the spatial context of orientation-awareness, effectively capturing the intricate relationships between shadows and ground objects. Here, we introduce a novel deep learning network named the Orientation-Aware and Multi-Scale Feature Fusion Network (OAMSFNet) due to noise reduction considerations. The proposed OAMSFNet comprises Shadow Aware Feature Encoder (SFE), Orientation-Aware Context Module (OCM), and Multi-Scale Feature Pyramid (MFP). Furthermore, a multi-scale feature fusion algorithm has been devised to enhance the detection and segmentation capabilities of the model in shadow regions. Finally, a comparative study was conducted on the Aerial Imagery dataset for Shadow Detection (AISD) in both quantitative and qualitative aspects. The experimental results show that our method outperforms state-of-the-art methods while maintaining a lightweight model design, indicating that our approach exhibits remarkable accuracy and stability in excellent agreement with predictions. OAMSFNet achieved an average F-score of 89.85%, surpassing the straightforward semantic segmentation model SegNet by 9.94%, and demonstrated remarkable efficiency enhancements, with FLOPs reduced to as low as 11.48 G, resulting in efficiency improvements of 66.7%-96.5% compared to other shadow detection models. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) open up new opportunities for transportation monitoring. However, the vehicle targets in UAV images are situated in dynamic scenarios, such as uncertain background, dramatically varying arrangement density, multi-scale, and arbitrary-oriented. Most strategies for UAV-based monitoring require complex manoeuvring and still lack accurate abilities and lightweight structures. Consequently, designing effective detection methods with both speed and accuracy is challenging. This paper proposes a lightweight YOLO-based arbitrary-oriented vehicle detector via precise positional information encoding and bidirectional feature fusion to address the above issues. First, an additional angular classification prediction branch is added to the YOLO head network to significantly improve the detection performance for arbitrary-oriented vehicles without incurring the extra computational complexity and burden. Second, a C3 module embedded coordinate attention (C3CA) is presented to capture long-range dependencies and preserve vehicles' precise positional information in feature maps. Then, a fully connected bidirectional feature fusion module (FC-BiFPN) is applied at the neck of the YOLO detection framework, which is helpful for multi-scale vehicle detection. This module can efficiently aggregate features at different resolutions and automatically enhance information interaction. Finally, experiments and comparisons on vehicle and remote sensing datasets demonstrate that our approach outperforms the state-of-the-art methods in balancing precision and efficiency. In addition, the overall network design follows the lightweight concept, which better meets the real-time requirements of the UAV urban traffic monitoring platform in realistic scenarios. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (RSCD) is crucial for our understanding of the dynamic pattern of the Earth's surface and human influence. Recently, transformer-based methodologies have advanced from their powerful global modeling capabilities in RSCD tasks. Nevertheless, they remain under excessive parameterization, which continues to be severely constrained by time and computation resources. Here, we present a transformer-based RSCD model called the Segmentation Multi-Branch Change Detection Network (SMBCNet). Our proposed approach combines a hierarchically structured transformer encoder with a cross-scale enhancement module (CEM) to extract global information with lower complexity. To account for the diverse nature of changes, we introduce a plug-and-play multi-branch change fusion module (MCFM) that integrates temporal features. Within this module, we transform the change detection task into a semantic segmentation problem. Moreover, we identify the Temporal Feature Aggregation Module (TFAM) to facilitate integrating features from diverse spatial scales. These results demonstrate that semantic segmentation is an effective solution to change detection (CD) problems in remote sensing images. [ABSTRACT FROM AUTHOR]

Deep learning has proven to be highly successful at semantic segmentation of remote sensing images (RSIs); however, it remains challenging due to the significant intraclass variation and interclass similarity, which limit the accuracy and continuity of feature recognition in land use and land cover (LULC) applications. Here, we develop a stage-adaptive selective network that can significantly improve the accuracy and continuity of multiscale ground objects. Our proposed framework can learn to implement multiscale details based on a specific attention method (SaSPE) and transformer that work collectively. In addition, we enhance the feature extraction capability of the backbone network at both local and global scales by improving the window attention mechanism of the Swin Transfer. We experimentally demonstrate the success of this framework through quantitative and qualitative results. This study demonstrates the strong potential of the prior knowledge of deep learning-based models for semantic segmentation of RSIs. [ABSTRACT FROM AUTHOR]

Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing (RS) applications. However, most approaches rarely distinguish the role of the body and edge of RS ground objects; thus, our understanding of these semantic parts has been frustrated by the lack of detailed geometry and appearance. Here we present a multiscale decoupled supervision network for RS semantic segmentation. Our proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge components. We further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image (RSI) ground objects, enabling new segmentation designs and semantic components that can learn to perform multiscale geometry and appearance. Our results outperform the previous algorithm and are robust to different datasets. These results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches. [ABSTRACT FROM AUTHOR]

Unsupervised representation learning plays an important role in remote sensing image applications. Generative adversarial network (GAN) is the most popular unsupervised learning method in recent years. However, due to poor data augmentation, many GAN-based methods are often difficult to carry out. In this paper, we propose an improved unsupervised representation learning model called multi-layer feature fusion Wasserstein GAN (MF-WGANs) which considers extracting the feature information for remote sensing scene classification from unlabelled samples. First, we introduced a multi-feature fusion layer behind the discriminator to extract the high-level and mid-level feature information. Second, we combined the loss of multi-feature fusion layer and WGAN-GP to generate more stable and high-quality remote sensing images with a resolution of 256 × 256. Finally, the multi-layer perceptron classifier (MLP-classifier) is used to classify the features extracted from the multi-feature fusion layer and evaluated with the UC Merced Land-Use, AID and NWPU-RESISC45 data sets. Experiments show that MF-WGANs has richer data augmentation and better classification performance than other unsupervised representation learning classification models (e.g., MARTA GANs). [ABSTRACT FROM AUTHOR]