Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains

Ye, Chengming; Li, Yao; Cui, Peng; Li, Liang; Pirasteh, Saied; Marcato, José; Gonçalves, Wesley Nunes; Li, Jonathan

doi:10.1109/jstars.2019.2951725

Cited by 120 publications

(55 citation statements)

References 95 publications

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“…We designed the CNN architecture using minimal input data for landslide detection in the study area. In recent studies, CNNs outperformed traditional machine learning algorithms in the detection of landslides (Liu et al 2020;Ye et al 2019). However, designing a CNN architecture and optimizing its parameters using sample strategies remain challenging tasks (Ghorbanzadeh et al 2019a).…”

Section: Discussionmentioning

confidence: 99%

“…Lei et al (2019) optimized FCN-PP (fully convolutional network within pyramid pooling) for landslide inventory mapping and compared the results with other models, such as the ELSE (employed edge-based level set evolution), RLSE (region-based level set evolution), CDMRF (change detection-based on Markov random field), and CDFFCM (change detection-based fast fuzzy c-means clustering). Ye et al (2019) used hyperspectral data for landslide detection using DLWC (deep learning with constraints), SID (spectral information divergence), SAM (spectral angle match), and SVM (support vector machine) (Eskandari et al 2020). Ghorbanzadeh et al (2019a) evaluated the performance of different CNN models for landslide detection and compared these with three other ML models, namely, ANN, SVM, and RF, using the elevation factor coupled with remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

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Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

et al. 2021

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Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

et al. 2021

View full text Add to dashboard Cite

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“…During the last decade, deep-learning models and, other machine learning methods, and CNNs have been applied successfully in broad range object detection aims [29]. A deep belief network along with a logistic regression classifier were used by [30] to detect landslides on hyperspectral images. [31,32] used freely available high-resolution Google Earth TM images for scattered shrub detection with a CNN model.…”

Section: Landslide Mapping Using Two Main Deep-learning Convolution Nmentioning

confidence: 99%

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Ghorbanzadeh

Meena

Shahabi

et al. 2021

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

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“…As the most mature DL framework, convolutional neural networks (CNN) have been widely used in geoscience domain, such as scene classification [36], land-cover classification [37][38][39][40], lithological facies classification [41,42], functional zone division [43] and ground target detection [44][45][46]. In recent years, CNN-based methods has been applied in landslide-related domain, especially in landslide detection [47][48][49][50][51][52][53][54][55][56]. To the best of authors' knowledge, only a few research use CNN-based methods for LSM [57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Chen

Ming

Xiao

et al. 2021

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

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Landslide susceptibility mapping (LSM) is an effective way to predict spatial probability of landslide occurrence. Existing convolutional neural networks (CNN)-based methods apply self-built CNN with simple structure, which failed to reach CNN's full potential on high-level feature extraction, meanwhile ignored the use of numerical predisposing factors. For the purpose of exploring feature fusion based CNN models with greater reliability in LSM, this study proposes an ensemble model based on channel-expanded pre-trained CNN and traditional machine learning model (CPCNN-ML). In CPCNN-ML, pre-trained CNN with mature structure is modified to excavate high-level features of multi-channel predisposing factor layers. LSM result is generated by traditional machine learning (ML) model based on hybrid feature of high-level features and numerical predisposing factors. Lantau Island, Hong Kong is selected as study area, temporal landslide inventory is used for model training and evaluation. Experimental results show that CPCNN-ML has ability to predict landslide occurrence with high reliability, especially the CPCNN-ML based on random forest (RF). Contrast experiments with self-built CNN and traditional ML models further embody the superiority of CPCNN-ML. It is worth noting that coastal regions are newly identified landslide-prone regions compared with previous research. This finding is of great reference value for Hong Kong authorities to formulate appropriate disaster prevention and mitigation policies.

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Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains

Cited by 120 publications

References 95 publications

Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

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