CEBSNet: Change-Excited and Background-Suppressed Network with Temporal Dependency Modeling for Bitemporal Change Detection

• URL: http://arxiv.org/abs/2505.15322v1
• Date: Wed, 21 May 2025 09:57:30 GMT
• Title: CEBSNet: Change-Excited and Background-Suppressed Network with Temporal Dependency Modeling for Bitemporal Change Detection
• Authors: Qi'ao Xu, Yan Xing, Jiali Hu, Yunan Jia, Rui Huang,
• Abstract summary: Change detection is a critical task in remote sensing and computer vision.<n>Current methods overlook temporal dependencies and overemphasize prominent changes.<n>We introduce textbfCEBSNet, a novel change-excited and background-suppressed network for change detection.
• Score: 5.667475728935794
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Change detection, a critical task in remote sensing and computer vision, aims to identify pixel-level differences between image pairs captured at the same geographic area but different times. It faces numerous challenges such as illumination variation, seasonal changes, background interference, and shooting angles, especially with a large time gap between images. While current methods have advanced, they often overlook temporal dependencies and overemphasize prominent changes while ignoring subtle but equally important changes. To address these limitations, we introduce \textbf{CEBSNet}, a novel change-excited and background-suppressed network with temporal dependency modeling for change detection. During the feature extraction, we utilize a simple Channel Swap Module (CSM) to model temporal dependency, reducing differences and noise. The Feature Excitation and Suppression Module (FESM) is developed to capture both obvious and subtle changes, maintaining the integrity of change regions. Additionally, we design a Pyramid-Aware Spatial-Channel Attention module (PASCA) to enhance the ability to detect change regions at different sizes and focus on critical regions. We conduct extensive experiments on three common street view datasets and two remote sensing datasets, and our method achieves the state-of-the-art performance.

Related papers

• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective [0.6749750044497732]
  We develop a fine-tuning strategy called the Semantic Change Network (SCN) to address the data scarcity issue.<n>We observe that the locations of changes between the two images are spatially identical, a concept we refer to as spatial consistency.<n>This enhances the modeling of multi-scale changes and helps capture underlying relationships in change detection semantics.
  arXiv  Detail & Related papers  (2025-03-26T17:15:43Z)
• A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences [4.370130821531168]
  Change detection in remote sensing imagery is a critical technique for Earth observation.<n>In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images.<n>In this study, we found that difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension.
  arXiv  Detail & Related papers  (2025-01-19T00:14:20Z)
• Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning [49.24306593078429]
  We propose a novel framework for remote sensing image change captioning, guided by Key Change Features and Instruction-tuned (KCFI) KCFI includes a ViTs encoder for extracting bi-temporal remote sensing image features, a key feature perceiver for identifying critical change areas, and a pixel-level change detection decoder. To validate the effectiveness of our approach, we compare it against several state-of-the-art change captioning methods on the LEVIR-CC dataset.
  arXiv  Detail & Related papers  (2024-09-19T09:33:33Z)
• Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model [62.337749660637755]
  We present change data generators based on generative models which are cheap and automatic. Changen2 is a generative change foundation model that can be trained at scale via self-supervision. The resulting model possesses inherent zero-shot change detection capabilities and excellent transferability.
  arXiv  Detail & Related papers  (2024-06-26T01:03:39Z)
• ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection [16.62779899494721]
  Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. We propose an effective Siamese-based framework to encode the semantic changes occurring in the bi-temporal RS images.
  arXiv  Detail & Related papers  (2024-04-26T17:47:14Z)
• Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification [79.9402521412239]
  We propose a novel Frequency Domain modality-invariant feature learning framework (FDMNet) to reduce modality discrepancy from the frequency domain perspective. Our framework introduces two novel modules, namely the Instance-Adaptive Amplitude Filter (IAF) and the Phrase-Preserving Normalization (PPNorm)
  arXiv  Detail & Related papers  (2024-01-03T17:11:27Z)
• TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images [64.63004710817239]
  We propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD. It improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. Our proposed method achieves a new state-of-the-art performance on four optical and two SAR image CD benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T07:42:19Z)
• dual unet:a novel siamese network for change detection with cascade differential fusion [4.651756476458979]
  We propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.
  arXiv  Detail & Related papers  (2022-08-12T14:24:09Z)
• TINYCD: A (Not So) Deep Learning Model For Change Detection [68.8204255655161]
  The aim of change detection (CD) is to detect changes occurred in the same area by comparing two images of that place taken at different times. Recent developments in the field of deep learning enabled researchers to achieve outstanding performance in this area. We propose a novel model, called TinyCD, demonstrating to be both lightweight and effective.
  arXiv  Detail & Related papers  (2022-07-26T19:28:48Z)
• 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)
• DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images [17.839181739760676]
  The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. We propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images.
  arXiv  Detail & Related papers  (2020-03-07T16:57:10Z)

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.