Feature Aggregation Network for Building Extraction from High-resolution Remote Sensing Images

• URL: http://arxiv.org/abs/2309.06017v1
• Date: Tue, 12 Sep 2023 07:31:51 GMT
• Title: Feature Aggregation Network for Building Extraction from High-resolution Remote Sensing Images
• Authors: Xuan Zhou, Xuefeng Wei
• Abstract summary: High-resolution satellite remote sensing data acquisition has uncovered the potential for detailed extraction of surface architectural features. Current methods focus exclusively on localized information of surface features. We propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features.
• Score: 1.7623838912231695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement in high-resolution satellite remote sensing data acquisition, particularly those achieving submeter precision, has uncovered the potential for detailed extraction of surface architectural features. However, the diversity and complexity of surface distributions frequently lead to current methods focusing exclusively on localized information of surface features. This often results in significant intraclass variability in boundary recognition and between buildings. Therefore, the task of fine-grained extraction of surface features from high-resolution satellite imagery has emerged as a critical challenge in remote sensing image processing. In this work, we propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features, thereby enabling the refined extraction of landmark buildings from high-resolution satellite remote sensing imagery. The Pyramid Vision Transformer captures these global features, which are subsequently refined by the Feature Aggregation Module and merged into a cohesive representation by the Difference Elimination Module. In addition, to ensure a comprehensive feature map, we have incorporated the Receptive Field Block and Dual Attention Module, expanding the receptive field and intensifying attention across spatial and channel dimensions. Extensive experiments on multiple datasets have validated the outstanding capability of FANet in extracting features from high-resolution satellite images. This signifies a major breakthrough in the field of remote sensing image processing. We will release our code soon.

Related papers

• Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion [9.098711843118629]
  This paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet) This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions. By incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features.
  arXiv  Detail & Related papers  (2024-10-08T02:17:38Z)
• An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution [0.5461938536945723]
  We propose an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) Our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones. Our model achieved superior performance compared to various existing models.
  arXiv  Detail & Related papers  (2024-05-07T18:15:51Z)
• Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement [49.15531684596958]
  We propose a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement. The first phase learns amplitude information to restore image brightness, and the second phase learns phase information to refine details. We have constructed two dark light remote sensing datasets to address the current lack of datasets in dark light remote sensing image enhancement.
  arXiv  Detail & Related papers  (2024-04-26T13:21:31Z)
• RS-Mamba for Large Remote Sensing Image Dense Prediction [58.12667617617306]
  We propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing images with linear complexity. Our model achieves better efficiency and accuracy than transformer-based models on large remote sensing images.
  arXiv  Detail & Related papers  (2024-04-03T12:06:01Z)
• Super-Resolving Face Image by Facial Parsing Information [52.1267613768555]
  Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. We build a novel parsing map guided face super-resolution network which extracts the face prior from low-resolution face image. High-resolution features contain more precise spatial information while low-resolution features provide strong contextual information.
  arXiv  Detail & Related papers  (2023-04-06T08:19:03Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images [0.09065034043031667]
  We propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible. Specifically, the dense connection is designed to fully utilize the representation from the different convolutional layers. Experiments on the drone-based datasets named VisDrone-DET suggest a competitive performance of our method.
  arXiv  Detail & Related papers  (2020-12-19T10:05:31Z)
• Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks [1.3764085113103222]
  The presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction. Our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals.
  arXiv  Detail & Related papers  (2020-07-06T22:54:02Z)
• Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network [6.371173732947292]
  We propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. Our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously.
  arXiv  Detail & Related papers  (2020-06-23T17:24:09Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.