Road extraction from satellite imagery is vital in a broad range of applications. However, extracting complete roads is challenging due to road occlusions caused by the surroundings. This paper proposed an improved encoder-decoder network via extracting road context and integrating full-stage features from satellite imagery, dubbed as RCFSNet. A multi-scale context extraction (MSCE) module is designed to enhance the inference capabilities by introducing adequate road context. Multiple full-stage feature fusion (FSFF) modules in the skip connection are devised to provide accurate road structure information, and we devise a coordinate dual attention mechanism (CDAM) to strengthen the representation of road features. Extensive experiments are carried out on two public datasets, as a result, our RCFSNet outperforms other state-of-the-art methods. The results indicate that the road labels extracted by our method have preferable connectivity. The source code will be available at https://github.com/CVer-Yang/RCFSNet.

Road extraction is a significant research hotspot in the area of remote sensing images. Extracting an accurate road network from remote sensing images is still challenging, because some objects in the images are similar to the road, and some results are discontinuous due to the occlusion. Recently, convolutional neural networks (CNNs) have shown their power in a road extraction process. However, the contextual information cannot be captured effectively by those CNNs. Based on CNNs, combining with high-level semantic features and foreground contextual information (FCI), a novel road extraction method for remote sensing images is proposed in this letter. First, the position attention (PA) mechanism is designed to enhance the expression ability for the road feature. Then, the contextual information extraction module (CIEM) is constructed to capture the road contextual information in the images. At last, an FCI supplement module (FCISM) is proposed to provide foreground context information at different stages of the decoder, which can improve the inference ability for the occluded area. Extensive experiments on the DeepGlobal road dataset showed that the proposed method outperforms the existing methods in accuracy, intersection over union (IoU), precision, and $F1$ score and yields competitive recall results, which demonstrated the efficiency of the new model.

Infrared small target detection (IRSTD) plays a critical role in applications such as night navigation and fire rescue. Its primary purpose is to extract small targets from cluttered backgrounds. While deep learning-based methods have made great advancements in this field, there are still some limitations. One common issue is that the detected target shape tends to be smooth, and extremely small targets may not be effectively detected due to background interference. This paper proposes an edge-guided perception network (EGPNet) for IRSTD to alleviate this trouble. To maintain the information of small targets, EGPNet utilizes a multiscale feature progressive fusion encoder to extract features. This progressive fusion manner enhances semantic information and contextual correlation. Considering that the detected target shapes may result in smoothing effect, an edge-guided image refinement module is incorporated to improve the integrity of the target shape. Moreover, we introduce a local target amplifier to boost the visibility and representation of targets, while suppressing the clutter background interference. The experimental results illustrate that the proposed model can detect the targets with small and weak in different scenes well. Our code is publicly available at https://github.com/qianngli/EGPNet.

Remote sensing image captioning aims to describe the crucial objects from remote sensing images in the form of natural language. The inefficient utilization of object texture and semantic features in images, along with the ineffective cross-modal alignment between image and text features, are the primary factors that impact the model to generate high-quality captions. To alleviate this trouble, this paper presents a network for remote sensing image captioning, namely HCNet, including hierarchical feature aggregation and cross-modal feature alignment. Specifically, a hierarchical feature aggregation module is proposed to obtain a comprehensive representation of vision features, which is beneficial for producing accurate descriptions. Considering the disparities between different modal features, we design a cross-modal feature interaction module in the decoder to facilitate feature alignment. It can fully utilize cross-modal features to localize critical objects. Besides, a cross-modal feature align loss is introduced to realize the alignment between image and text features. Extensive experiments show our HCNet can achieve satisfactory performance. Especially, we demonstrate significant performance improvements of +14.15% CIDEr score on NWPU datasets compared to existing approaches. The source code is publicly available at https://github.com/CVer-Yang/HCNet.

Road extraction from remote sensing images holds significant application value in various aspects of daily scenarios. However, it is still challenging to extract high-quality road results from remote sensing images due to the interference of objects sharing similar structures with roads in the background, and the occlusion caused by surroundings. To alleviate these problems, a road extraction network based on the global-local Context perception and Cross spatial-scale feature interaction is proposed (C ² Net). First, a global-local context perception module is incorporated to capture the overall topology features of road, which aims to improve the ability of model to discriminate between roads and similar objects. Then, the cross spatail-scale feature interaction module is designed in the skip connection to effectively aggregate full-scale features without loss of feature information, which can provide rich and accurate road structural features for the decoder. Experiments conducted on public road datasets demonstrate that C ² Net outperforms existing methods in terms of comprehensive metrics such as Intersection over Union (IoU) and F1-score. The results indicate that C ² Net can produce road results with superior connectivity and quality. The source code will be publicly available at https://github.com/CVer-Yang/CCNet.

Image captioning is a fundamental vision-language task with wide-ranging applications in daily life. Existing methods often struggle to accurately interpret the semantic information in remote sensing images due to the complexity of backgrounds. Target region masks can effectively reflect the shape characteristics of targets and their potential interrelationships. Therefore, incorporating and fully integrating these features can significantly improve the quality of generated captions. However, researchers are hindered by the lack of relevant datasets that contain corresponding object masks. It is natural to ask: How to efficiently introduce and utilize object masks? In this paper, we provide potential target masks for the publicly available remote sensing image caption (RSIC) datasets, enabling models to utilize the regional features of targets for RSIC. Meanwhile, a novel RSIC algorithm is proposed that combines regional positional features with fine-grained semantic information, abbreviated as S2CPNet. To effectively capture the semantic information from image and position relationship from mask respectively, the semantic and spatial feature enhance sub-modules are introduced at the ends of encoder branches, respectively. Furthermore, the cross view feature fusion module is designed to integrate regional features and semantic information efficiently. Then, a target recognition decoder is developed to enhance the ability of model to identify and describe critical targets in images. Finally, we improve the caption generation decoder by adaptively merging textual information with visual features to generate more accurate descriptions. Our model achieve satisfactory results on three RSIC datasets compared with existing method. The related datasets and code will be open-sourced in https://github.com/CVer-Yang/SSCPNet.