Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation

Lv, Zhi Yong; Shi, Wenzhong; Zhang, Xiaokang; Benediktsson, Jón Atli

doi:10.1109/jstars.2018.2803784

Cited by 110 publications

(64 citation statements)

References 55 publications

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“…For the second stage, inspired by previous studies [14,49,50], a multi-scale segment voting decision method was developed as a post-processing fusion strategy. Multi-scale segmentation based on the post-event image was acquired using eCognition to ensure that the image was constructed in an object manner ("object" is a group of pixels homogeneous in spectra domain and connected continuously in the spatial domain [66]).…”

Section: Multi-scale Segmentation Voting Decisionmentioning

confidence: 99%

“…This technique concentrates on finding and capturing land cover changes using two or more remote sensing images that cover the same geographic area acquired on different dates [1,[4][5][6]. LCCD plays an important role in large-scale land use analysis [7][8][9], environment monitoring evaluation [10,11], natural hazard assessment [12][13][14], and natural resource inventory [15]. However, issues such as "salt-and-pepper" noise in the detection results, especially for VHR remote sensing images [16][17][18], pose a challenge in the practical applications of LCCD with remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Zheng

Cao

et al. 2019

Remote Sensing

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In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.

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Section: Multi-scale Segmentation Voting Decisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Zheng

Cao

et al. 2019

Remote Sensing

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“…Five pairs of bi-temporal images on A-E areas in Hong Kong, were captured by the Zeiss RMK TOP 15 Aerial Survey Camera System at a flying height of approximately 2.4km on December 2007 and on November 2014, respectively [3]. Due to the geometrical resolution of bi-temporal images is 0.5m, the captured images have a large size.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Most of them depend on change detection that aims to detect the changed information of target at areas by analyzing the multi-temporal images acquired in different time of the same geographical area [2]. The popular ones can be roughly divided into three categories: threshold-based approaches [3,4], approaches based on feature extraction and feature classification [5][6][7], and deep learning approaches [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping

Lei

Zhang

Xue

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel approach based on a symmetric fully convolutional network within pyramid pooling (FCN-PP) for landslide mapping (LM). The proposed approach has three advantages. Firstly, this approach is automatic and insensitive to noise because multivariate morphological reconstruction (MMR) is used for image preprocessing. Secondly, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected pyramid pooling module addresses the drawback of global pooling employed by convolutional neural network (CNN), fully convolutional network (FCN), U-Net, etc. Experimental results show that the proposed FCN-PP is effective for LM, and it outperforms state-of-the-art approaches in terms of four metrics, P recision, Recall, F-score, and Accuracy.

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“…Object-oriented approaches have also been suggested to support feature extraction tasks. Such methodologies that leverage the application of object-based analysis to optical imagery primarily apply segmentation techniques to form objects composed of neighboring pixels with similar textural and color features; then, objects that capture the patterns produced by landslides can be detected using classification techniques [15,16]. Similar to previous pixel-based approaches, these methodologies have also been developed and validated using specific landslide types.…”

Section: Introductionmentioning

confidence: 99%

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

et al. 2020

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The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.Keywords: landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery

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Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation

Cited by 110 publications

References 55 publications

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

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