Deep Fusion of DOM and DSM Features for Benggang Discovery
Benggang is a typical erosional landform in southern and southeastern China. Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cann...
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description | Benggang is a typical erosional landform in southern and southeastern China. Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. The two sources of information (DOM and DSM) are fused via a gated neural network, which learns the most discriminative features for the detection of benggang. The evaluation of a challenging benggang dataset demonstrates that our method exceeds several baselines, even with limited training examples. The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks. |
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Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. The two sources of information (DOM and DSM) are fused via a gated neural network, which learns the most discriminative features for the detection of benggang. The evaluation of a challenging benggang dataset demonstrates that our method exceeds several baselines, even with limited training examples. The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks.</description><identifier>ISSN: 2220-9964</identifier><identifier>EISSN: 2220-9964</identifier><identifier>DOI: 10.3390/ijgi10080556</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>Artificial neural networks ; benggang ; CNN ; Computer Science ; Computer Science, Information Systems ; Datasets ; Deep learning ; Deformation ; Detection ; DOM ; DSM ; Economics ; Embedding ; Feature extraction ; fusion ; Geography, Physical ; Geomorphology ; Imagery ; Land degradation ; Landforms ; Landslides & mudslides ; Machine learning ; Neural networks ; Physical Geography ; Physical Sciences ; Remote Sensing ; Science & Technology ; Sustainable development ; Technology ; Terrain ; Training ; Unmanned aerial vehicles ; Vegetation</subject><ispartof>ISPRS international journal of geo-information, 2021-08, Vol.10 (8), p.556, Article 556</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. 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The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks.</description><subject>Artificial neural networks</subject><subject>benggang</subject><subject>CNN</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Deformation</subject><subject>Detection</subject><subject>DOM</subject><subject>DSM</subject><subject>Economics</subject><subject>Embedding</subject><subject>Feature extraction</subject><subject>fusion</subject><subject>Geography, Physical</subject><subject>Geomorphology</subject><subject>Imagery</subject><subject>Land degradation</subject><subject>Landforms</subject><subject>Landslides & mudslides</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Physical Geography</subject><subject>Physical Sciences</subject><subject>Remote 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Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. The two sources of information (DOM and DSM) are fused via a gated neural network, which learns the most discriminative features for the detection of benggang. The evaluation of a challenging benggang dataset demonstrates that our method exceeds several baselines, even with limited training examples. The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks.</abstract><cop>BASEL</cop><pub>Mdpi</pub><doi>10.3390/ijgi10080556</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-0683-4669</orcidid><orcidid>https://orcid.org/0000-0003-1573-4536</orcidid><orcidid>https://orcid.org/0000-0001-6964-0354</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Artificial neural networks benggang CNN Computer Science Computer Science, Information Systems Datasets Deep learning Deformation Detection DOM DSM Economics Embedding Feature extraction fusion Geography, Physical Geomorphology Imagery Land degradation Landforms Landslides & mudslides Machine learning Neural networks Physical Geography Physical Sciences Remote Sensing Science & Technology Sustainable development Technology Terrain Training Unmanned aerial vehicles Vegetation |
title | Deep Fusion of DOM and DSM Features for Benggang Discovery |
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