Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images

• URL: http://arxiv.org/abs/2410.09539v1
• Date: Sat, 12 Oct 2024 14:01:41 GMT
• Title: Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images
• Authors: Yi Xiao, Bin Luo, Jun Liu, Xin Su, Wei Wang,
• Abstract summary: Change Detection (CD) methods still struggle to address pseudo changes resulting from domain information differences in multi-temporal images. We propose a bi-temporal Gaussian distribution feature-dependent network (BGFD) to address this problem. BGFD has effectively reduced pseudo changes and enhanced the detection capability of detail information.
• Score: 17.674955288033974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change Detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to address pseudo changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. To address this, we propose a bi-temporal Gaussian distribution feature-dependent network (BGFD). Specifically, we first introduce the Gaussian noise domain disturbance (GNDD) module, which approximates distribution using image statistical features to characterize domain information, samples noise to perturb the network for learning redundant domain information, addressing domain information differences from a more fundamental perspective. Additionally, within the feature dependency facilitation (FDF) module, we integrate a novel mutual information difference loss ($L_{MI}$) and more sophisticated attention mechanisms to enhance the capabilities of the network, ensuring the acquisition of essential domain information. Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo changes and enhanced the detection capability of detail information. It has also achieved state-of-the-art performance on four publicly available datasets - DSIFN-CD, SYSU-CD, LEVIR-CD, and S2Looking, surpassing baseline models by +8.58%, +1.28%, +0.31%, and +3.76% respectively, in terms of the F1-Score metric.

Related papers

• Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
  Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
  arXiv  Detail & Related papers  (2024-07-18T04:36:10Z)
• DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning [5.932234366793244]
  Change sensing (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images. Existing RSCD methods are devoted to contextual modeling in the spatial domain to enhance the changes of interest. We propose DNet, a RSCD network based on dual-domain learning (i.e. frequency and spatial domains)
  arXiv  Detail & Related papers  (2024-06-19T14:54:09Z)
• Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery [7.767708235606408]
  We propose a new network, Siamese-U2Net Feature Differential Meets Network (SMDNet) This network combines the Siam-U2Net Feature Differential (SU-FDE) and the denoising diffusion implicit model to improve the accuracy of image edge change detection. Our method's combination of feature extraction and diffusion models demonstrates effectiveness in change detection in remote sensing images.
  arXiv  Detail & Related papers  (2024-01-17T16:48:55Z)
• BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation [4.659935767219465]
  The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition.
  arXiv  Detail & Related papers  (2024-01-09T02:53:06Z)
• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment [59.831917206058435]
  Domain adaptive detection aims to improve the generalization of detectors on target domain. Recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. We introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning.
  arXiv  Detail & Related papers  (2023-01-01T08:38:07Z)
• IDAN: Image Difference Attention Network for Change Detection [3.5366052026723547]
  We propose a novel image difference attention network (IDAN) for remote sensing image change detection. IDAN considers the differences in regional and edge features of images and thus optimize the extracted image features. The experimental results demonstrate that the F1-score of IDAN improves 1.62% and 1.98% compared to the baseline model on WHU dataset and LEVIR-CD dataset.
  arXiv  Detail & Related papers  (2022-08-17T13:46:13Z)
• Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images [60.89777029184023]
  We propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD.
  arXiv  Detail & Related papers  (2022-04-18T17:59:01Z)
• Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions [20.88671966047938]
  Change detection plays a vital role in ecological protection and urban planning. Traditional subpixel-based methods for change detection using images with different resolutions may lead to substantial error accumulation. We propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module.
  arXiv  Detail & Related papers  (2021-02-27T11:17:40Z)
• 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)

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.