Automatic recognition of landslide based on CNN and texture change detection

Ding, Anzi; Zhang, Qingyong; Zhou, Xiaojian; Dai, Bicheng

doi:10.1109/yac.2016.7804935

Cited by 100 publications

(69 citation statements)

References 7 publications

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“…In the last few years, CNNs have been successfully applied to many classical image processing problems, such as denoising [50], super-resolution [51], pansharpening [8,24], segmentation [52], object detection [53,54], change detection [27] and classification [17,[55][56][57]. The main strengths of CNNs are (i) an extreme versatility that allows them to approximate any sort of linear or non-linear transformation, including scaling or hard thresholding; (ii) no need to design handcrafted filters, replaced by machine learning; (iii) high-speed processing, thanks to parallel computing.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…-multi-temporal: is one of the most investigated forms of fusion in remote sensing due to the rich information content hidden in the temporal dimension. In particular, it can be applied to strictly time-related tasks, like prediction [13], change detection [27][28][29] and co-registration [30], and general-purpose tasks, like segmentation [7], despeckling [31] and feature extraction [32][33][34], which do not necessarily need a joint processing of the temporal sequence, but can benefit from it. -multi-sensor: is gaining an ever growing importance due both to the recent deployment of many new satellites and to the increasing tendency of the community to share data.…”

Section: Introductionmentioning

confidence: 99%

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A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

et al. 2018

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Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account-optical sequences, SAR sequences, digital elevation model-so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

et al. 2018

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“…Besides these classic change detection methods, the deep learning methods have been applied to remote sensing data (e.g., multispectral [50][51][52][53], hyperspectral [54], synthetic aperture radar (SAR) [55]) to classify land cover types such as forests [56], rivers and farmland [54] and landslides [57], and to detect land cover changes. These have obtained better performances than classic methods.…”

Section: Introductionmentioning

confidence: 99%

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

et al. 2019

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We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm's robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods-including the most recent CNN-based and generative adversarial network (GAN)-based ones-have only reached 25% AP in their best cases.

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“…To obtain better results, Liu et al [13] proposed a new deep convolutional coupling network that is fully unsupervised with-out using any labels. To apply deep learning technologies to landslide recognition, Ding et al [14] used convolutional neural network (CNN) and texture change detection to recognize landslides. Because CNN employs multiple pooling layers and a fully connectional layer to achieve classification tasks, the final result is coarse and has a low recognition accuracy.…”

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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Automatic recognition of landslide based on CNN and texture change detection

Cited by 100 publications

References 7 publications

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

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

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