Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

Li, Jinglun; Xiu, Jiapeng; Yang, Zhengqiu; Liu, Chen

doi:10.3390/ijgi9100571

Cited by 23 publications

(9 citation statements)

References 44 publications

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“…Nevertheless, the abovementioned methods exclusively account for the attention relations of a single dimension and ignore the dependencies of other dimensions in the calculation process. To obtain multidimensional dependency information, SCAttNet [42] and DPA-Net [43] consider two dimensions of attention, channel and spatial, to refine features adaptively using a lightweight attention mechanism. Furthermore, HMANet [39] considers the attention of the category dimension for calibrating the category information.…”

Section: B Semantic Segmentation On the Base Of Self-attention Mechanismmentioning

confidence: 99%

Enhancing Efficient Global Understanding Network With CSWin Transformer for Urban Scene Images Segmentation

Zhang,

Shao,

Qiao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The global context is crucial to the semantic segmentation task of remote sensing (RS) urban scene imagery since objects have large size variations, high similarity, and mutual occlusion. However, the existing methods for extracting global context information have limitations when directly applied to very highresolution RS images, mainly in high complexity of computation and memory consumption. To alleviate this limitation, we propose a novel Efficient Global Understanding semantic segmentation Network (EGUNet) to extract global context information efficiently for applicability to RS images. Specifically, EGUNet is a hybrid U-shaped architecture of convolutional neural networks (CNNs) and Transformer in which the encoder uses the CSWin Transformer to capture global semantic information, and the decoder uses the CNNs structure to recover local detail information. Thus, the proposed EGUNet has a powerful global extraction capability and local position information recovery capability. In addition, three effective modules are proposed to improve the segmentation accuracy to make EGUNet more applicable for urban scene image segmentation tasks. First, a feature adaptive fusion module is introduced in the decoder to improve the fusion of the deep semantics and the location detail features. Second, an adaptive atrous-spatial pyramid pooling is designed at the skip connections to enhance the multiscale understanding of high-level semantic context. Finally, we introduce a lightweight enhanced segmentation head to utilize the information from each decoder stage for segmentation. Extensive experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the exceptional segmentation accuracy of EGUNet, outperforming the state-of-the-art methods.

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Section: B Semantic Segmentation On the Base Of Self-attention Mechanismmentioning

confidence: 99%

Enhancing Efficient Global Understanding Network With CSWin Transformer for Urban Scene Images Segmentation

Zhang,

Shao,

Qiao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Zuo et al 35 proposed a multiscale deformable attention net using the improved spatial attention mechanism for RSI segmentation. Li et al 36 proposed a dual path attention network with channel attention and spatial attention for RSI segmentation. Li et al 37 proposed a semantic segmentation network with spatial and channel attention mechanisms for high-resolution RSIs.…”

Section: Mixed Attentionmentioning

confidence: 99%

“…proposed a multiscale deformable attention net using the improved spatial attention mechanism for RSI segmentation. Li et al 36 . proposed a dual path attention network with channel attention and spatial attention for RSI segmentation.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised domain adaptation using modified cycle generative adversarial network for aerial image classification

Ren

Jia

Yue

et al. 2022

J. Appl. Rem. Sens.

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.The unsupervised domain adaptation (UDA) of aerial image classification is of great significance in the traffic management and public safety applications. Due to the differences in the distribution of source and target domain datasets from different sensors and cities, and the acquisition difficulty and high annotation cost of target domain datasets, existing domain adaptive methods have difficulty obtaining high accuracy and stability. In this work, a modified cycle generative adversarial network (CycleGAN) (MGAN) based on the multiscale residual block (MSRB) and multipooling coordinate attention (MPCA) modules is proposed to generate fake images, which is the most critical stage of the UDA method. In the MGAN, the MSRB module can extract the multiscale information of the image, and the MPCA module can fuse the position and channel information in the image. These two modules better extract the features of edges, regions and semantic classes, which makes the generated fake images better retain the information of the source domain and possess the style of the target domain. Otherwise, the Deeplabv3+ model is used to obtain a pretrained and final segmentation model in the first and third stages. In the experiment, the proposed method is validated on the aerial images of the Potsdam and Vaihingen datasets in the International Society for Photogrammetry and Remote Sensing (ISPRS) two-dimensional semantic labeling benchmark and improves overall accuracy from 52% to 60% on the segmentation of the target domain, compared with the existing optimal method; it also achieves an accuracy of 77% for buildings. The results demonstrate that the proposed method can achieve a higher accuracy and stability.

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“…(iii) Image segmentation: also known as semantic segmentation refers to labeling each pixel in the image, usually using end-to-end At-DL methods. From the At-DL papers, 17 papers addressed image segmentation [103][104][105]. (iv) Image fusion: is mostly known as a fundamental preprocess in the RS field, and aims to produce higher spectral and spatial resolutions.…”

Section: Overview Of the Reviewed Papersmentioning

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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Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

Cited by 23 publications

References 44 publications

Enhancing Efficient Global Understanding Network With CSWin Transformer for Urban Scene Images Segmentation

Enhancing Efficient Global Understanding Network With CSWin Transformer for Urban Scene Images Segmentation

Unsupervised domain adaptation using modified cycle generative adversarial network for aerial image classification

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

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