Hard Region Aware Network for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2305.19513v2
• Date: Fri, 18 Oct 2024 05:14:57 GMT
• Title: Hard Region Aware Network for Remote Sensing Change Detection
• Authors: Zhenglai Li, Chang Tang, Xinwang Liu, Xingchen Hu, Xianju Li, Ning Li, Changdong Li,
• Abstract summary: Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. This paper proposes a novel change detection network, termed as HRANet, which provides accurate change maps via hard region mining.
• Score: 44.269913858088614
• License:
• Abstract: Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. Numerous CD methods have been proposed, and considerable results have been achieved recently. However, detecting changes in hard regions, i.e., the change boundary and irrelevant pseudo changes caused by background clutters, remains difficult for these methods, since they pose equal attention for all regions in bi-temporal images. This paper proposes a novel change detection network, termed as HRANet, which provides accurate change maps via hard region mining. Specifically, an online hard region estimation branch is constructed to model the pixel-wise hard samples, supervised by the error between predicted change maps and corresponding ground truth during the training process. A cross-layer knowledge review module is introduced to distill temporal change information from low-level to high-level features, thereby enhancing the feature representation capabilities. Finally, the hard region aware features extracted from the online hard region estimation branch and multi-level temporal difference features are aggregated into a unified feature representation to improve the accuracy of CD. Experimental results on two benchmark datasets demonstrate the superior performance of HRANet in the CD task.

Related papers

• Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection [82.65760006883248]
  We introduce a new task named Change Detection Question Answering and Grounding (CDQAG) CDQAG extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. We construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks.
  arXiv  Detail & Related papers  (2024-10-31T11:20:13Z)
• 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)
• Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference [26.095501974608908]
  We develop a novel weakly supervised change detection technique via Knowledge Distillation and Multiscale Sigmoid Inference (KD-MSI) Our proposed technique, with its integrated training strategy, significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-03-09T05:01:51Z)
• 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)
• Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review [45.78958623050146]
  Change detection is an essential and widely utilized task in remote sensing. It aims to detect and analyze changes occurring in the same geographical area over time. Deep learning has emerged as a powerful tool for feature extraction and addressing these challenges.
  arXiv  Detail & Related papers  (2023-05-09T23:52:37Z)
• Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks [35.415260892693745]
  We propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Our end-to-end network establishes a novel framework by aggregating retrieved features and feature pairs from different layers.
  arXiv  Detail & Related papers  (2023-04-03T16:01:03Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• Local Relation Learning for Face Forgery Detection [73.73130683091154]
  We propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions. We also propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation.
  arXiv  Detail & Related papers  (2021-05-06T10:44:32Z)
• 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.