MCHA-Net: A multi-end composite higher-order attention network guided with hierarchical supervised signal for high-resolution remote sensing image change detection

Zhang, Haiming; Ma, Guorui; Zhang, Yongxian; Wang, Huijiao; Li, Heng; Fan, Leland L.

doi:10.1016/j.isprsjprs.2023.05.033

Cited by 9 publications

(3 citation statements)

References 54 publications

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“…The change gradient image (CGI) [36] first embeds a multiscale information attentional module in U-Net to achieve multiscale information and adds the position channel attention module to pay more attention to the spectral and spatial information in the multiscale fused feature map. A composite higher-order attention network with multiple encoding paths named MCHA-Net [37] can improve the generalizability and detection accuracy of the network and outperforms state-of-the-art methods in both visual interpretation and quantitative evaluation. An unsupervised single-temporal change detection framework based on intraand inter-image patch exchange (I3PE) [38] allows for training deep-change detectors on unpaired and unlabeled single-temporal remote-sensing images that are readily available in real-world applications.…”

Section: Deep-learning Methods For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

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Section: Deep-learning Methods For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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“…Several papers have proposed different approaches that incorporate attention mechanisms in change detection, including the hierarchical attention network [26], supervised attention [27], and channel self-attention [28]. Moreover, Zhang et al developed the Dual Cross-Attention-Transformer (DCAT) method, which utilizes the cross-attention mechanism to improve change feature discrimination and merges bi-temporal features [29]. Some works have explored multi-scale attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Oubara,

Wu,

Maleki

et al. 2024

IJGI

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Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

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“…Most previous studies in this area fall into the categories of image segmentation [11,12] or object detection [13]. The insights offered include but are not limited to, the effectiveness of "attention mechanisms" and visual transformers built on those mechanisms [14,15,16,17,18], novel convolutional blocks [19] and even graph neural networks that account for dependencies between structure types and their damage conditions [20]. Other studies use additional data-for example, public building footprint inventories such as OpenStreetMap 2 for building localization [21], or as additional input data channels [22,23]-, or use both pre-and post-event images for damage identification through change detection strategies [24].…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment---Application to 2023 Kahramanmaraş Earthquake Sequence

Soleimani-Babakamali,

Askari,

Heravi

et al. 2023

Preprint

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Extensive impacted region and government damage surveys following the 2023 Kahramanmaraş earthquake sequence provided a unique opportunity to design and validate a rapid post-event damage assessment framework with satellite images, addressing the typical challenges (e.g., generalization to unseen areas) encountered in rapid regional assessment efforts. By analyzing images from the significantly impacted city of Antakya, we manually identified visual features and compared those with damage states in the survey. Then, an Artificial Intelligence-based framework was developed to automate segmentation and damage assessment processes for future test areas. The resulting tool, dubbed RAPID-A, is an uncertainty-aware ensemble of various deep segmentation models fed with public data released shortly after such incidents. Furthermore, the extent of the damages reported in the survey allowed us to propose and validate a strategy to identify various intensities of damage using a single model, considering the less severe damage as more uncertain. Test case studies in Gaziantep and Kahramanmaraş demonstrate that RAPID-A is generalizable, accurate, and efficient. The significance of this framework lies in its ability to assist in rapid initial damage evaluations before expert dispatch or to replace it in countries that lack the capacity to launch large-scale regional assessment programs to guide foreign support.

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MCHA-Net: A multi-end composite higher-order attention network guided with hierarchical supervised signal for high-resolution remote sensing image change detection

Cited by 9 publications

References 54 publications

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment---Application to 2023 Kahramanmaraş Earthquake Sequence

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