Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

Knevels, Raphael; Petschko, Helene; Leopold, Philip; Brenning, Alexander

doi:10.3390/ijgi8120551

Cited by 27 publications

(24 citation statements)

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“…The OBIA methods usually use segmentation algorithm to obtain the objects first and then use the objects for subsequent image analysis. Currently, the OBIA methods are widely applied in multi-scale research [15,16], change detection [17] and landslide detection [18]. To better understand ecological patterns, it is also expanded to the species-level mapping of vegetation [19].…”

Section: Related Workmentioning

confidence: 99%

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

Cui

Meng

et al. 2021

Remote Sensing

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The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) semantic segmentation to a certain extent. Comparing with the pixel-based methods, the graph neural networks (GNNs) usually use objects as input nodes, so they not only have relatively small computational complexity, but also can carry richer semantic information. However, the traditional GNNs are more rely on the context information of the individual samples and lack geographic prior knowledge that reflects the overall situation of the research area. Therefore, these methods may be disturbed by the confusion of “different objects with the same spectrum” or “violating the first law of geography” in some areas. To address the above problems, we propose a remote sensing semantic segmentation model called knowledge and spatial pyramid distance-based gated graph attention network (KSPGAT), which is based on prior knowledge, spatial pyramid distance and a graph attention network (GAT) with gating mechanism. The model first uses superpixels (geographical objects) to form the nodes of a graph neural network and then uses a novel spatial pyramid distance recognition algorithm to recognize the spatial relationships. Finally, based on the integration of feature similarity and the spatial relationships of geographic objects, a multi-source attention mechanism and gating mechanism are designed to control the process of node aggregation, as a result, the high-level semantics, spatial relationships and prior knowledge can be introduced into a remote sensing semantic segmentation network. The experimental results show that our model improves the overall accuracy by 4.43% compared with the U-Net Network, and 3.80% compared with the baseline GAT network.

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Section: Related Workmentioning

confidence: 99%

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

Cui

Meng

et al. 2021

Remote Sensing

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“…OBIA, in contrast to PBA, utilizes a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest [7,10,11,68]. In OBIA, individual landslides are considered an ensemble of pixels, rather than individual pixels that are spatially unrelated [13,68,69]. In our study area, because landslides did not generate explicit and visible land cover changes, the application of optical data solely would be ineffective; thus, we integrated these data with a DEM.…”

Section: Related Studiesmentioning

confidence: 99%

“…In certain conditions, such as densely vegetated terrain, field-based investigation is ineffective or even impossible [9]. Benefiting from an abundant collection of remote sensing (RS) data, automatic approaches have been introduced to landslide studies by various scientists [1,[3][4][5][6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Among the automatic methods, pixel-based (PBA) [4,5,14,16,19] and object-based (OBIA) [7,8,[10][11][12][13]15,20] classification methods can be distinguished.…”

Section: Introductionmentioning

confidence: 99%

“…Benefiting from an abundant collection of remote sensing (RS) data, automatic approaches have been introduced to landslide studies by various scientists [1,[3][4][5][6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Among the automatic methods, pixel-based (PBA) [4,5,14,16,19] and object-based (OBIA) [7,8,[10][11][12][13]15,20] classification methods can be distinguished. Different studies have compared the performance of OBIA and PBA in various RS applications [29][30][31], including landslide detection [20,32,33].…”

Section: Introductionmentioning

confidence: 99%

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On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Pawłuszek-Filipiak

Borkowski

2020

Remote Sensing

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Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies.

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“…Tavakkoli Piralilou et al [27] combined object-oriented segmentation with the neural network and random forest for LM, and the optimal scale parameters were considered in the segmentation. Knevels et al [28] investigated the potential of opensource geographic information system for object-based LM. The object-based methods were shown to provide LM results with fewer false positives and well-retained geometry compared with pixel-based methods [11].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

2021

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Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. 1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. 2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. 3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.

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Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

Cited by 27 publications

References 50 publications

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

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