A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images

• URL: http://arxiv.org/abs/2102.08041v1
• Date: Tue, 16 Feb 2021 09:26:31 GMT
• Title: A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images
• Authors: Junzheng Wu, Biao Li, Yao Qin, Weiping Ni, Han Zhang and Yuli Sun
• Abstract summary: Change detection (CD) in remote sensing images has been an ever-expanding area of research. In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images.
• Score: 12.823633963080281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection (CD) in remote sensing images has been an ever-expanding area of research. To date, although many methods have been proposed using various techniques, accurately identifying changes is still a great challenge, especially in the high resolution or heterogeneous situations, due to the difficulties in effectively modeling the features from ground objects with different patterns. In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images. First, the object-wise high level features are obtained through a pre-trained U-net and the multiscale segmentations. Treating each parcel as a node, the graph representations can be formed and then, fed into the proposed multiscale graph convolutional network with each channel corresponding to one scale. The multiscale GCN propagates the label information from a small number of labeled nodes to the other ones which are unlabeled. Further, to comprehensively incorporate the information from the output channels of multiscale GCN, a fusion strategy is designed using the father-child relationships between scales. Extensive Experiments on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method outperforms some state-of the-art methods in both qualitative and quantitative evaluations. Besides, the Influences of some factors are also discussed.

Related papers

• MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations [37.845442465099396]
  Most post-processing methods build a model of the nuisances from the target observations themselves. We propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. We apply the proposed algorithm to several datasets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off.
  arXiv  Detail & Related papers  (2024-09-23T09:22:45Z)
• Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks [0.10995326465245926]
  Graph convolutional networks (GCNs) have emerged as a powerful alternative to multiple instance learning with convolutional neural networks in digital pathology. Our proposed multi-scale GCN (MS-GCN) tackles this issue by leveraging information across multiple magnification levels in whole slide images. MS-GCN demonstrates superior performance over existing single-magnification GCN methods.
  arXiv  Detail & Related papers  (2024-03-22T09:48:50Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification [42.15709954199397]
  A transformer-based heterogeneously salient graph representation (THSGR) approach is proposed in this paper. First, a multimodal heterogeneous graph encoder is presented to encode distinctively non-Euclidean structural features from heterogeneous data. A self-attention-free multi-convolutional modulator is designed for effective and efficient long-term dependency modeling.
  arXiv  Detail & Related papers  (2023-11-17T04:06:20Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images [12.222830717774118]
  A dual neighborhood hypergraph neural network is proposed in this article. The proposed method comprises better effectiveness and robustness compared to many state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-02-27T02:39:08Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.