Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning
142 citations recorded by the source corpus.
Abstract Geological disaster recognition, especially, landslide recognition, is of vital importance in disaster prevention, disaster monitoring and other applications. As more and more optical remote sensing images are available in recent years, landslide recognition on optical remote sensing images is in demand. Therefore, in this paper, we propose a deep learning based landslide recognition method for optical remote sensing images. In order to capture more distinct features hidden in landslide images, a particular wavelet transformation is proposed to be used as the preprocessing method. Next, a corrupting & denoising method is proposed to enhance the robustness of the model in recognize landslide features. Then, a deep auto-encoder network with multiple hidden layers is proposed to learn the high-level features and representations of each image. A softmax classifier is used for class prediction. Experiments are conducted on the remote sensing images from Google Earth. The experimental results indicate that the proposed wavDAE method outperforms the state-of-the-art classifiers both in efficiency and accuracy.
Subfields: Artificial Intelligence, Management, Monitoring, Policy and Law
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In Artificial Intelligence
- Chaopeng Shen
- Bernard Kamsu-Foguem
- Ala Al-Fuqaha
- Mohsen Guizani
- Sameh Sorour
- Qiangqiang Yuan
- Fana Tangara
- Boukaye Boubacar Traore
In Management, Monitoring, Policy and Law
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Optical remote sensing images that are fed to a deep AEs network and softmax classifiers were proposed by Liu and Wu [150] to predict geological landslides.”
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “In disaster detection and categorization studies, researchers have started to employ DL to detect wildfires (Lee et al., 2017; Sharma et al., 2017; Q. Zhang, Xu, et al., 2016) and landslides from remote sensing images (Ying Liu & Wu, 2016).”
- Deep convolution neural network for image recognition (2018, background): “…intelligence and relevant for several domains: the audible or visual signal analysis, facial recognition (Russakovsky et al., 2015), disaster recognition (Liu and Wu, 2016), voice recognition, computer vision (Karpathy et al., 2014) and automated language processing (Hinton et al., 2006).”
- Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Liu and Wu (2016) adopted AE to identify geological disasters and found that AE outperforms the state-of-the-art classifiers in terms of efficiency and accuracy. Yuan and Jia (2015) proposed a water quality assessment method based on sparse AE and”