Joint Grid-Based Attention and Multilevel Feature Fusion for Landslide Recognition

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-04 DOI:10.1109/JSTARS.2024.3491216

Xinran Li;Tao Chen;Gang Liu;Jie Dou;Ruiqing Niu;Antonio Plaza

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引用次数: 0

Abstract

Landslide recognition (LR) is a fundamental task for disaster prevention and control. Convolutional neural networks (CNNs) and transformer architectures have been widely used for extracting landslide information. However, CNNs cannot accurately characterize long-distance dependencies and global information, while the transformer may not be as effective as CNNs in capturing local features and spatial information. To address these limitations, we construct a new LR network based on grid-based attention and multilevel feature fusion (GAMTNet). We complement CNNs by adding a transformer-based structure in a layer-by-layer fashion and improving methods for sequence generation and attention weight calculation. As a result, GAMTNet effectively learns global and local information about landslides across various spatial scales. We evaluated our model using landslide data collected from the southwest region of Jiuzhaigou County, Aba Tibetan, and Qiang Autonomous Prefecture, Sichuan Province, China. The results demonstrate that the proposed GAMTNet model achieves an F 1-score of 0.8951, a Kappa coefficient of 0.8807, and an MIoU of 0.8908, indicating its capability for the accurate landslide identification and its potential application in LR tasks.

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基于网格的联合关注和多层次特征融合用于滑坡识别

滑坡识别（LR）是灾害预防和控制的一项基本任务。卷积神经网络（CNN）和变换器架构已被广泛用于提取滑坡信息。然而，卷积神经网络无法准确描述长距离依赖关系和全局信息，而变换器在捕捉局部特征和空间信息方面可能不如卷积神经网络有效。为了解决这些局限性，我们构建了一种基于网格关注和多级特征融合的新型 LR 网络（GAMTNet）。我们通过逐层添加基于变换器的结构，并改进序列生成和注意力权重计算方法，对 CNN 进行了补充。因此，GAMTNet 可有效学习不同空间尺度上滑坡的全局和局部信息。我们使用从中国四川省阿坝藏族羌族自治州九寨沟县西南地区收集到的滑坡数据对我们的模型进行了评估。结果表明，所提出的 GAMTNet 模型的 F1 分数为 0.8951，Kappa 系数为 0.8807，MIoU 为 0.8908，这表明该模型具有准确识别滑坡的能力，并有望应用于 LR 任务中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.