An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery

• URL: http://arxiv.org/abs/2105.04132v1
• Date: Mon, 10 May 2021 06:23:27 GMT
• Title: An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
• Authors: Xuan Yang, Shanshan Li, Zhengchao Chen, Jocelyn Chanussot, Xiuping Jia, Bing Zhang, Baipeng Li, Pan Chen
• Abstract summary: We propose a novel convolutional neural network architecture, named attention-fused network (AFNet) We achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and the ISPRS Potsdam 2D dataset.
• Score: 26.362854938949923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset.

Related papers

• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• Multi-Scale Direction-Aware Network for Infrared Small Target Detection [2.661766509317245]
  Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. We propose a multi-scale direction-aware network (MSDA-Net) to integrate the high-frequency directional features of infrared small targets. MSDA-Net achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST and IRSTD-1k datasets.
  arXiv  Detail & Related papers  (2024-06-04T07:23:09Z)
• LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network [2.028685490378346]
  We propose a novel location refined feature pyramid network (LR-FPN) to enhance the extraction of shallow positional information. Experiments on two large-scale remote sensing datasets demonstrate that the proposed LR-FPN is superior to state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2024-04-02T03:36:07Z)
• Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment [61.45639694373033]
  We propose a novel lightweight network for optical remote sensing images (ORSI-SOD) based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, and a portable decoder for inference.
  arXiv  Detail & Related papers  (2023-01-07T04:33:51Z)
• Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers [5.177947445379688]
  We propose a new segmentation model that combines convolutional neural networks with deep transformers. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-06-20T12:03:54Z)
• TC-Net: Triple Context Network for Automated Stroke Lesion Segmentation [0.5482532589225552]
  We propose a new network, Triple Context Network (TC-Net), with the capture of spatial contextual information as the core. Our network is evaluated on the open dataset ATLAS, achieving the highest score of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm.
  arXiv  Detail & Related papers  (2022-02-28T11:12:16Z)
• Specificity-preserving RGB-D Saliency Detection [103.3722116992476]
  We propose a specificity-preserving network (SP-Net) for RGB-D saliency detection. Two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-18T14:14:22Z)
• Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images [10.835342317692884]
  The accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks. This paper proposes a Multi-Attention-Network (MANet) to address these issues. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention.
  arXiv  Detail & Related papers  (2020-09-03T09:08:02Z)
• Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving [94.40107679615618]
  This paper proposes a novel binarized driveable area detection network (binary DAD-Net) It uses only binary weights and activations in the encoder, the bottleneck, and the decoder part. It outperforms state-of-the-art semantic segmentation networks on public datasets.
  arXiv  Detail & Related papers  (2020-06-15T07:09:01Z)
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks [57.0502745301132]
  We propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Our micro-dense block can be integrated with neural architecture search based models to boost their performance.
  arXiv  Detail & Related papers  (2020-04-19T08:34:52Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.