WRICNet: A Weighted Rich-Scale Inception Coder Network for Remote Sensing Image Change Detection

Yu, Jinhui; Hu, Lei; Zhang, Yongmei; Xin, Ying

doi:10.1109/tgrs.2022.3145652

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Some recent works have started to explore approaches to improve the accuracy of building edge change detection, such as by introducing additional branches to detect the edges of building change areas [25][26][27]. Although these methods improve the edge accuracy of change detection to some extent, capturing the edge information from complexly distributed grounding objects remains challenging: (1) Buildings often have similar color characteristics to their surrounding regions, making it challenging to capture the edge of changed buildings; (2) Buildings usually have shadows, which can cause the edges of the buildings to be difficult to identify.…”

Section: Introductionmentioning

confidence: 99%

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

Yang,

Zhou,

Feng

et al. 2024

Applied Sciences

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Building change detection monitors building changes by comparing and analyzing multi-temporal images acquired from the same area and plays an important role in land resource planning, smart city construction and natural disaster assessment. Different from change detection in conventional scenes, buildings in the building change detection task usually appear in a densely distributed state, which is easy to be occluded; at the same time, building change detection is easily interfered with by shadows generated by light and similar-colored features around the buildings, which makes the edges of the changed region challenging to be distinguished. Aiming at the above problems, this paper utilizes edge information to guide the neural network to learn edge features related to changes and suppress edge features unrelated to changes, so as to accurately extract building change information. First, an edge-extracted module is designed, which combines deep and shallow features to supplement the lack of feature information at different resolutions and to extract the edge structure of the changed features; second, an edge-guided module is designed to fuse the edge features with different levels of features and to guide the neural network to focus on the confusing building edge regions by increasing the edge weights to improve the network’s ability to detect the edges that have changed. The proposed building change detection algorithm has been validated on two publicly available data (WHU and LEVIR-CD building change detection datasets). The experimental results show that the proposed model achieves 91.14% and 89.76% in F1 scores, respectively, demonstrating superior performance compared to some recent learning change detection methods.

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Section: Introductionmentioning

confidence: 99%

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

Yang,

Zhou,

Feng

et al. 2024

Applied Sciences

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“…CNN-based change detection methods transform the two temporal remote sensing images into high-level features, extract semantic context of change regions by fusing the features of the two time-phased images, and mitigate artificial errors stemming from preprocessing. There are two types of CNN-based change detection methods, categorized based on the fusion strategy utilized: image-level fusion [25][26][27][28][29] and feature-level fusion [30][31][32][33][34][35][36][37]. In image-level fusion networks, the two remote sensing images from different temporal input as a whole into CNN to obtain a representation of image differences.…”

Section: Introductionmentioning

confidence: 99%

MFSFNet: Multi-Scale Feature Subtraction Fusion Network for Remote Sensing Image Change Detection

Huang

You

2023

Remote Sensing

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Change detection plays a crucial role in remote sensing by identifying surface modifications between two sets of temporal remote sensing images. Recent advancements in deep learning techniques have yielded significant achievements in this field. However, there are still some challenges: (1) Existing change feature fusion methods often introduce redundant information. (2) The complexity of network structures leads to a large number of parameters and difficulties in model training. To overcome these challenges, this paper proposes a Multi-Scale Feature Subtraction Fusion Network (MFSF-Net). It comprises two primary modules: the Multi-scale Feature Subtraction Fusion (MFSF) module and the Feature Deep Supervision (FDS) module. MFSF enhances change features and reduces redundant pseudo-change features. FDS provides additional supervision on different scales of change features in the decoder, improving the training efficiency performance of the network. Additionally, to address the problem of imbalanced samples, the Dice loss strategy is introduced as a means to mitigate this issue. Through comprehensive experiments, MFSF-Net achieves an F1 score of 91.15% and 95.64% on LEVIR-CD and CDD benchmark datasets, respectively, outperforming six state-of-the-art algorithms. Moreover, it attains an improved balance between model complexity and performance, showcasing the efficacy of the proposed approach.

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“…Compared with traditional models, CNN-based models have powerful nonlinear mapping capabilities, enabling them to capture detailed image information and complex texture features. According to the fusion strategy, CNN-based change detection networks are divided into early fusion [17], [18], [19], [20] and late fusion [21], [22], [23], [24] networks. The early fusion networks input multitemporal images as a whole to the deep CNN.…”

mentioning

confidence: 99%

Adaptive Spatial and Difference Learning for Change Detection

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Change detection refers to revealing the surface changes from multitemporal images of a given scene, where changed regions of different size and shape may appear anywhere. Convolutional neural network, as one of the most widely used deep learning architectures, has shown good performance in change detection task recently. However, due to multiple convolution and pooling operators in deep architectures, it often happens that small changes are missed and sharp edges are blurred. In this article, an adaptive spatial and difference learning method is proposed to tackle this problem, which mainly contains a feature-adapted difference learning (FADL) module and a difference recalibration (DR) module. In FADL, a kernel-adapted spatial learning part is constructed to capture varying spatial information, which selects proper kernels to adapt different shape and size of changed regions; a joint feature refinement part is employed to learn comprehensive features between bitemporal data, which are further enhanced by difference information. In addition, DR with shallow features is designed to recalibrate spatial and difference details further. From the experiments on three public datasets, our proposed method can relieve the spatial ambiguity problem and obtains the optimal results among the compared change detection methods.

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WRICNet: A Weighted Rich-Scale Inception Coder Network for Remote Sensing Image Change Detection

Cited by 8 publications

References 29 publications

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

MFSFNet: Multi-Scale Feature Subtraction Fusion Network for Remote Sensing Image Change Detection

Adaptive Spatial and Difference Learning for Change Detection

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