SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective

• URL: http://arxiv.org/abs/2503.20734v1
• Date: Wed, 26 Mar 2025 17:15:43 GMT
• Title: SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective
• Authors: Ziyu Zhou, Keyan Hu, Yutian Fang, Xiaoping Rui,
• Abstract summary: 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.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Change detection is a key task in Earth observation applications. Recently, deep learning methods have demonstrated strong performance and widespread application. However, change detection faces data scarcity due to the labor-intensive process of accurately aligning remote sensing images of the same area, which limits the performance of deep learning algorithms. To address the data scarcity issue, we develop a fine-tuning strategy called the Semantic Change Network (SCN). We initially pre-train the model on single-temporal supervised tasks to acquire prior knowledge of instance feature extraction. The model then employs a shared-weight Siamese architecture and extended Temporal Fusion Module (TFM) to preserve this prior knowledge and is fine-tuned on change detection tasks. The learned semantics for identifying all instances is changed to focus on identifying only the changes. Meanwhile, we observe that the locations of changes between the two images are spatially identical, a concept we refer to as spatial consistency. We introduce this inductive bias through an attention map that is generated by large-kernel convolutions and applied to the features from both time points. This enhances the modeling of multi-scale changes and helps capture underlying relationships in change detection semantics. We develop a binary change detection model utilizing these two strategies. The model is validated against state-of-the-art methods on six datasets, surpassing all benchmark methods and achieving F1 scores of 92.87%, 86.43%, 68.95%, 97.62%, 84.58%, and 93.20% on the LEVIR-CD, LEVIR-CD+, S2Looking, CDD, SYSU-CD, and WHU-CD datasets, respectively.

Related papers

• Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection [41.80924135539708]
  We propose a Semantic-Aware Change Detection network, namely SA-CDNet. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes. We also design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes.
  arXiv  Detail & Related papers  (2024-12-22T08:27:15Z)
• Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms [27.882122236282054]
  We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2.<n>We evaluate our approach on two benchmark datasets, VL-CMU-CD and PSCD, along with their viewpoint-varied versions.<n>Our experiments demonstrate significant improvements in F1-score, particularly in scenarios involving geometric changes between image pairs.
  arXiv  Detail & Related papers  (2024-09-25T11:55:27Z)
• 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)
• Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization [10.059696915598392]
  We propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets.
  arXiv  Detail & Related papers  (2023-11-19T11:30:43Z)
• Transformer-based Multimodal Change Detection with Multitask Consistency Constraints [10.906283981247796]
  Current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. We propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection.
  arXiv  Detail & Related papers  (2023-10-13T17:38:45Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images [22.72105435238235]
  We propose a Semantic Change (SCanFormer) to explicitly model the 'from-to' semantic transitions between the bi-temporal RSIss. Then, we introduce a semantic learning scheme to leverage the Transformer-temporal constraints, which are coherent to the SCD task, to guide the learning of semantic changes. The resulting network (SCanNet) outperforms the baseline method in terms of both detection of critical semantic changes and semantic consistency in the obtained bi-temporal results.
  arXiv  Detail & Related papers  (2022-12-10T08:49:19Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Distribution Alignment: A Unified Framework for Long-tail Visual Recognition [52.36728157779307]
  We propose a unified distribution alignment strategy for long-tail visual recognition. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
  arXiv  Detail & Related papers  (2021-03-30T14:09:53Z)
• 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)
• A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation [50.261472059743845]
  Action Units (AUs) are geometrically-based atomic facial muscle movements. We propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. A Heatmap models whether an AU occurs or not at a given spatial location.
  arXiv  Detail & Related papers  (2020-04-14T16:51:13Z)

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.