dual unet:a novel siamese network for change detection with cascade differential fusion

• URL: http://arxiv.org/abs/2208.06293v1
• Date: Fri, 12 Aug 2022 14:24:09 GMT
• Title: dual unet:a novel siamese network for change detection with cascade differential fusion
• Authors: Kaixuan Jiang, Ja Liu, Fang Liu, Wenhua Zhang, Yangguang Liu
• Abstract summary: 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.
• Score: 4.651756476458979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, 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. In order to improve the generalization of networks, it computes the attention weights between any pixels between bitemporal images and uses them to engender more discriminating features. In order to improve the feature fusion and avoid gradient vanishing, multi-scale weighted variance map fusion strategy is proposed in the decoding stage. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.

Related papers

• Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning [71.14084801851381]
  Change captioning aims to succinctly describe the semantic change between a pair of similar images. Most existing methods directly capture the difference between them, which risk obtaining error-prone difference features. We propose a distractors-immune representation learning network that correlates the corresponding channels of two image representations.
  arXiv  Detail & Related papers  (2024-07-16T13:00:33Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• 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)
• A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detection [6.906936669510404]
  We propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks. The DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.
  arXiv  Detail & Related papers  (2023-10-03T08:26:27Z)
• 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)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• 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)
• Robust Data Hiding Using Inverse Gradient Attention [82.73143630466629]
  In the data hiding task, each pixel of cover images should be treated differently since they have divergent tolerabilities. We propose a novel deep data hiding scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets.
  arXiv  Detail & Related papers  (2020-11-21T19:08:23Z)
• 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)
• Unsupervised Change Detection in Satellite Images with Generative Adversarial Network [20.81970476609318]
  We propose a novel change detection framework utilizing a special neural network architecture -- Generative Adversarial Network (GAN) to generate better coregistered images. The optimized GAN model would produce better coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy.
  arXiv  Detail & Related papers  (2020-09-08T10:26:04Z)
• 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.