Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images

Zhang, Tianyang; Zhang, Xiangrong; Zhu, Peng; Tang, Xu; Li, Chen; Jiao, Licheng; Zhou, Huiyu

doi:10.1109/tcyb.2021.3096185

Cited by 30 publications

(17 citation statements)

References 57 publications

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“…Lee et al [35] proposed a spatial attention-guided instance segmentation framework CenterMask, which introduces the attention branch to establish the relationship between different objects. To mine semantic feature information, Zhang et al [36] proposed a novel semantic attention mechanism for instance segmentation. Zhao et al [37] proposed a synergistic attention mechanism to improve the instance segmentation accuracy for tiny object categories.…”

Section: B Visual Attention Mechanism For Instance Segmentationmentioning

confidence: 99%

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Wang

Zhang

et al. 2024

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

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Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-ofthe-art methods in various evaluation metrics. Code is available at https://github.com/darkseid-arch/SonarRPFNet.

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Section: B Visual Attention Mechanism For Instance Segmentationmentioning

confidence: 99%

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Wang

Zhang

et al. 2024

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

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“…In order to strengthen the learning of local key features in the spatial domain and spectral domain of hyperspectral images, the Resnet [64] introduces a hyperspectral image feature extraction method based on spatial-spectral attention on the basis of a convolutional network and uses a calculation to obtain the mask and identifies the features required for classification and improves the representation ability of hyperspectral. In remote sensing image instance segmentation, Zhang et al [65] proposed a semantic attention module, using additional segmentation supervision for attention, the activation values of instances under complex remote sensing noise background are significantly improved. working in a closed set, SPRL can achieve a self-evolution classification via an interaction with the environment.…”

Section: Attention Mechanismmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

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

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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“…First, a weighted fusion and refinement (WFR) module accounts for feature enhancement. It adaptively weighs the multi-level features produced by FPN and leverages the attention mechanism [25][26][27][28] to refine the fused features. Second, a lightweight affine transformation-based feature decoupling (ATFD) module produces decoupled features for subsequent classification and localization.…”

Section: Introductionmentioning

confidence: 99%

Recalibrating Features and Regression for Oriented Object Detection

et al. 2023

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The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method.

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Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images

Cited by 30 publications

References 57 publications

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Recalibrating Features and Regression for Oriented Object Detection

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