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ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Mingqiang Guo, Zhongyang Yu, Yongyang Xu, Ying Huang, Chunfeng Li
2021 Remote Sensing  
Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove.  ...  Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area.  ...  neural network for training the mangrove extraction network (ME-Net).  ... 
doi:10.3390/rs13071292 fatcat:nnmjjvhrcvdw7hce2qoprz5jai

Table of contents

2020 IEEE Geoscience and Remote Sensing Letters  
Yahia 2100 Hyperspectral Data Band Selection With the Explanatory Gradient Saliency Maps of Convolutional Neural Networks ...................... ........................................................  ...  ., multispectral and hyperspectral images) have provided an effective tool for mapping the mangrove distribution.  ... 
doi:10.1109/lgrs.2020.3037586 fatcat:437rgyi2lfbnfa4dkhsyvrrfs4

Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery

Nguyen Thanh Doan, E. Zavalishina
2021 E3S Web of Conferences  
This paper presents methodology of using deep convolutional neural network model to determine the position of shoreline on Sentinel 2 satellite image.  ...  Suggestions are also given for the number of files in the training dataset, as well as the information used for model training to solve the shoreline detection problem.  ...  A common deep convolutional neural network model for solving image segmentation problems often has ten million to hundreds of millions of such parameters.  ... 
doi:10.1051/e3sconf/202131004002 fatcat:hkffjiko6vd7na3w4pcsm2p27a

Deep Learning for Remote Sensing Image Understanding

Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, Xue Cheng Tai
2016 Journal of Sensors  
Huang explores the synergetic neural networks optimized by an improved quantum particle swarm algorithm for mangroves classification. The paper by Q.  ...  Lv et al. introduces deep belief networks to extract effective contextual mapping features for the task of PolSAR image classification. The paper by W.  ...  We appreciate all the authors for their submissions, as well as all the reviewers for their careful and professional review.  ... 
doi:10.1155/2016/7954154 fatcat:yoyzkgdi25er5hqggp4qub7plu

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification.  ...  Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data

Yujuan Guo, Jingjuan Liao, Guozhuang Shen
2021 Remote Sensing  
This study uses Landsat data combined with Capsules-Unet to map the dynamics of mangrove changes over the 25 years (1990–2015) along the MSR.  ...  In view of the capability of deep learning in processing massive data in recent years, we developed a new deep learning model—Capsules-Unet, which introduces the capsule concept into U-net to extract mangroves  ...  Acknowledgments: The authors are grateful to those colleagues who assisted with the field surveys and data collection, and would like to express thanks to the anonymous reviewers for their voluntary work  ... 
doi:10.3390/rs13020245 fatcat:h46z6ttwhjbelmnyptqg6vxy74

Residual Multi-Attention Classification Network for A Forest Dominated Tropical Landscape Using High-Resolution Remote Sensing Imagery

Tong Yu, Wenjin Wu, Chen Gong, Xinwu Li
2021 ISPRS International Journal of Geo-Information  
Remote sensing (RS) can support effective monitoring and mapping approaches for tropical forests, and to facilitate this we propose a deep neural network with an encoder–decoder architecture here to classify  ...  To deal with the complexity of tropical landscapes, this method utilizes a multi-scale convolution neural network (CNN) to expand the receptive field and extract multi-scale features.  ...  Among deep learning methods, various convolutional neural network (CNN) models have gained high popularity for scene classification [8] [9] [10] [11] [12] .  ... 
doi:10.3390/ijgi10010022 fatcat:7lerhcmexbhsrla577zdqglebe

Review on Semantic Segmentation of Satellite Images Using Deep Learning

Chandra Pal Kushwah
2021 International Journal for Research in Applied Science and Engineering Technology  
Improving the performance of deep learning models in a broad range of vision applications, important work has recently been carried out to evaluate approaches for deep learning models in image segmentation.In  ...  Also, provide Semantic Segmentation, Satellite imageries, and Deep learning & its Techniques like-DNN, CNN, RNN, RBM, and so on.CNN is one of the efficient deep learning techniques among all of them that  ...  CNN[28] (Transferable Object-Based Framework Based on Deep Convolutional Neural Networks for Building Extraction) 2019 OBIA 95% IEEE Journal of Selected Topics in Applied Earth Observations and  ... 
doi:10.22214/ijraset.2021.37204 fatcat:nroebivs7nfsndxazsuhrmiece

Advances in Scene Classification of Remotely Sensed High Resolution Images and the Existing Datasets

Research on Scene classification of remotely sensed images has shown a significant improvement in the recent years as it is used in various applications such as urban planning, urban mapping, management  ...  With the usage of different deep learning architecture and the availability of various high resolution image datasets, the field of Remote Sensing Scene Classification of high resolution (RSSCHR) images  ...  features on its own using deep learning neural network architectures.  ... 
doi:10.35940/ijitee.j8841.0881019 fatcat:7dznp4cr7zfv5m25y662dskuse


A. K. Brand, A. Manandhar
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In contrast, deep learning offers an end-to-end solution for image analysis and semantic segmentation.  ...  The use of remote sensing data for burned area mapping hast led to unprecedented advances within the field in recent years.  ...  Among deep neural networks, convolutional neural networks (CNNs) show the most promising potential as they are designed to handle spatially dependent data such as images especially well (LeCun et al.,  ... 
doi:10.5194/isprs-archives-xliii-b3-2021-47-2021 fatcat:oktorl2hbfdpjnag4wynhlvysy

Deep Learning Techniques for Geospatial Data Analysis [chapter]

Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam
2020 Learning and Analytics in Intelligent Systems  
Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such  ...  (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques  ...  Fig. 1 . 1 Deep Neural Network. Fig. 2 . 2 Convolutional Neural Network. Fig. 3 . 3 Recurrent Neural Network. Fig. 4 . 4 Auto-Encoders Table 1.  ... 
doi:10.1007/978-3-030-49724-8_3 fatcat:yv6stldjcjbclbfcx3d6i3s2um

A Convolutional Neural Network Based on Grouping Structure for Scene Classification

Xuan Wu, Zhijie Zhang, Wanchang Zhang, Yaning Yi, Chuanrong Zhang, Qiang Xu
2021 Remote Sensing  
A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable  ...  In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB  ...  Acknowledgments: The authors are grateful to the anonymous reviewers for their constructive comments and suggestions to improve this manuscript; the graduate students in Wanchang Zhang's group for open  ... 
doi:10.3390/rs13132457 fatcat:lzvgd3flsrf6hdqu3jnuwuyd2a

Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia's Mangroves Using Deep Learning

Davide Lomeo, Minerva Singh
2022 Remote Sensing  
Three multi-class classification convolutional neural network (CNN) models were generated, showing F1-score values as high as 0.9 in only six epochs of training.  ...  The proposed framework is believed to provide a robust, low-cost, cloud-based, near-real-time monitoring tool that could serve governments, environmental agencies, and researchers, to help map mangroves  ...  Acknowledgments: We are grateful to the Centre for Environmental Policy (CEP) for permitting us to undertake the research. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14102291 fatcat:jekdsstjgne3jjmlvsjdsxo3ma

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Mohammad Pashaei, Hamid Kamangir, Michael J. Starek, Philippe Tissot
2020 Remote Sensing  
In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation.  ...  Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances  ...  Acknowledgments: The authors gratefully acknowledge James Rizzo and Jacob Berryhill of the Conrad Blucher Institute for Surveying and Science for providing UAS field work support.  ... 
doi:10.3390/rs12060959 fatcat:xxt2aqsbabfj5gxy5hotwtre5q

On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition [article]

Jie Chen, Chao Yuan, Min Deng, Chao Tao, Jian Peng, Haifeng Li
2017 arXiv   pre-print
The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hierarchical coding directly from raw image.  ...  Thus, we train the deep neural network called AlexNet on our large scale remote sensing image recognition benchmark.  ...  Deep convolutional neural network (DCNN) is a biologically inspired multi-stage architecture composed of convolution and pooling, which are conceptually similar to simple and complex cells in the brain  ... 
arXiv:1708.01420v1 fatcat:bnjn5yjvy5d45ptrqpo2ekmjty
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