Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism

Gao, Hao; Cao, Lin; Yu, Dingfeng; Xiong, Xuejun; Bai, Xingzhen

doi:10.1109/access.2020.3013898

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…At-DL methods can be classified based on the used attention types (i.e., channel and spatial attention networks) as explained in Section 2 (Figure 10). The combined use of the channel and spatial attention mechanisms were the most frequently used types in the papers [59,127,128]. In addition, the channel type, which is mostly used in hyperspectral image processing [129][130][131], and the spatial type [47,132,133] were also solely used in 41 and 33 papers, respectively.…”

Section: Rq3 Which Types Of Attention Mechanisms Were Used In Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Rq3 Which Types Of Attention Mechanisms Were Used In Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…Figure 1 shows the GLPO-Net algorithm model. First, we extracted the features [7] of three subsets (beach, island and sea_ice) of ocean remote sensing images in the NWPU-RESISC45 dataset [43]. Second, these three groups of features were jointly input into the independent recurrent neural network under the global attention mechanism to obtain global features.…”

Section: Related Researchmentioning

confidence: 99%

Pixel-Level Prediction for Ocean Remote Sensing Image Features Fusion Based on Global and Local Semantic Relations

et al. 2021

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With the rapid development of remote-sensing imaging technology, remote-sensing images have become increasingly diverse, and people are paying more attention to ocean remote-sensing research. Because ocean remote-sensing data are complex, and the ocean environment is diverse, results will differ, even if the same target is detected at different times in the same scene. To obtain more semantic features and better pixel-level prediction capabilities, this paper proposes a pixel-level ocean remote-sensing image algorithm (GLPO-Net) that combines local and global features. First, texture features, color features, and spatial relationship features are extracted. Second, the algorithm constructs a multiscale local cross-attention mechanism strategy to obtain feature weight information in different directions to fully mine the local features of ocean remote-sensing images. Concurrently, an algorithm constructs a multiscale global cross-attention mechanism strategy to obtain global features. Then, the fusion of global features and local features is described in each submodule to obtain more representative deep features. Finally, small-sample ocean remote-sensing is described via image pixel-level prediction. The algorithm proposed in this paper has been tested with three public ocean remote-sensing datasets. The experimental results show that the proposed GLPO-Net algorithm can learn features from small samples of ocean remote-sensing images. Compared to the prediction results of other remote-sensing image algorithms, GLPO-Net exhibits better prediction capabilities. INDEX TERMS Ocean remote sensing, deep learning, features fusion, multi-scale convolutional.

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“…Currently, remote sensing image semantic segmentation has been widely used in practical application scenarios such as urban planning [2], natural resource management [3], disaster assessment [4], and agricultural production [5], [6]. It can provide highly accurate and efficient application solutions in various fields [7], [8], [9], [10]. However, the high-resolution urban remote sensing images contain a large amount of complex information, which hinders the extraction of global structure and semantic information of targets.…”

Section: Introductionmentioning

confidence: 99%

Local-Global Feature Capture and Boundary Information Refinement Swin Transformer Segmentor for Remote Sensing Images

Lin,

Zhang,

Zhu

et al. 2024

IEEE Access

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Semantic segmentation of urban remote sensing images is a highly challenging task. Due to the complex background, occlusion overlap, and small-scale targets in urban remote sensing images, the semantic segmentation results suffer from deficiencies such as similar target confusion, blurred target boundaries, and small-scale target omission. To solve the above problems, a local-global feature capture and boundary information refinement Swin Transformer segmentor (LGBSwin) is proposed. First, the dual linear attention module (DLAM) utilizes spatial linear attention and channel linear attention mechanisms for strengthening global modeling capabilities to improve the segmentation ability of similar targets. Second, boundary-aware enhancement (BAE) adaptively mines the boundary semantic information through the effective integration of high-level and low-level features to alleviate blurred boundaries. Finally, feature refinement aggregation (FRA) establishes information relationships between different layers, reduces the loss of local information, and enhances local-global dependence, thus significantly improving the recognition ability of small targets. Experimental results demonstrate the effectiveness of LGBSwin, with an F1 of 91.02% on the ISPRS Vaihingen dataset and 93.35% on the ISPRS Potsdam dataset.

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Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism

Cited by 11 publications

References 34 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Pixel-Level Prediction for Ocean Remote Sensing Image Features Fusion Based on Global and Local Semantic Relations

Local-Global Feature Capture and Boundary Information Refinement Swin Transformer Segmentor for Remote Sensing Images

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