Bi-Temporal Semantic Reasoning for the Semantic Change Detection of HR Remote Sensing Images

• URL: http://arxiv.org/abs/2108.06103v1
• Date: Fri, 13 Aug 2021 07:28:09 GMT
• Title: Bi-Temporal Semantic Reasoning for the Semantic Change Detection of HR Remote Sensing Images
• Authors: Lei Ding, Haitao Guo, Sicong Liu, Lichao Mou, Jing Zhang and Lorenzo Bruzzone
• Abstract summary: We propose a novel CNN architecture for semantic change detection (SCD) We elaborate on this architecture to model the bi-temporal semantic correlations. The resulting Bi-temporal Semantic Reasoning Network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations.
• Score: 17.53683781109742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic change detection (SCD) extends the change detection (CD) task to provide not only the change locations but also the detailed semantic categories (before and after the observation intervals). This fine-grained change information is more useful in land-cover/land-use (LC/LU) applications. Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the connections between the temporal branches and the change branch are weak. To overcome these limitations, we propose a novel CNN architecture for the SCD, where the temporal features are re-used and are deeply merged in the temporal branch. Furthermore, we elaborate on this architecture to model the bi-temporal semantic correlations. The resulting Bi-temporal Semantic Reasoning Network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improves the segmentation of both semantic categories and the changed areas. The codes in this paper are accessible at: https://github.com/ggsDing/Bi-SRNet

Related papers

• A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection [32.112311027857636]
  We propose a novel late-stage bitemporal feature fusion network to address the issue of semantic change detection. Specifically, we propose local global attentional aggregation module to strengthen feature fusion, and propose local global context enhancement module to highlight pivotal semantics. Our proposed model achieves new state-of-the-art performance on both datasets.
  arXiv  Detail & Related papers  (2024-06-15T16:02:10Z)
• SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection [9.682463974799893]
  Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. We propose SRC-Net: a bi-temporal spatial relationship concerned network for CD.
  arXiv  Detail & Related papers  (2024-06-09T06:53:39Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection [24.249552791014644]
  Change detection is a widely adopted technique in remote sense imagery (RSI) analysis. We propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling.
  arXiv  Detail & Related papers  (2023-05-30T03:39:53Z)
• 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)
• Robust Change Detection Based on Neural Descriptor Fields [53.111397800478294]
  We develop an object-level online change detection approach that is robust to partially overlapping observations and noisy localization results. By associating objects via shape code similarity and comparing local object-neighbor spatial layout, our proposed approach demonstrates robustness to low observation overlap and localization noises.
  arXiv  Detail & Related papers  (2022-08-01T17:45:36Z)
• BDA-SketRet: Bi-Level Domain Adaptation for Zero-Shot SBIR [52.78253400327191]
  BDA-SketRet is a novel framework performing a bi-level domain adaptation for aligning the spatial and semantic features of the visual data pairs. Experimental results on the extended Sketchy, TU-Berlin, and QuickDraw exhibit sharp improvements over the literature.
  arXiv  Detail & Related papers  (2022-01-17T18:45:55Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)
• Spatio-Temporal Representation Factorization for Video-based Person Re-Identification [55.01276167336187]
  We propose Spatio-Temporal Representation Factorization module (STRF) for re-ID. STRF is a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-07-25T19:29:37Z)
• BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification [86.73532136686438]
  We present an efficient spatial-temporal representation for video person re-identification (reID) We propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. BiCnet-TKS outperforms state-of-the-arts with about 50% less computations.
  arXiv  Detail & Related papers  (2021-04-30T06:44:34Z)
• Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images [28.683734356006262]
  We propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection. To the best of our knowledge, it is the first time that such a domain adaptation-based deep network is proposed for change detection.
  arXiv  Detail & Related papers  (2020-04-13T02:15:04Z)

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.