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3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Shunping Ji, Chi Zhang, Anjian Xu, Yun Shi, Yulin Duan
2018 Remote Sensing  
This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images.  ...  First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data.  ...  We appreciate Meng Lu from Utrecht University for editing the manuscript and Kebao Liu  ... 
doi:10.3390/rs10010075 fatcat:izqaipqm2zcu3ioocik7p7lwku

3D Fully Convolutional Neural Networks with Intersection Over Union Loss for Crop Mapping from Multi-Temporal Satellite Images [article]

Sina Mohammadi, Mariana Belgiu, Alfred Stein
2021 arXiv   pre-print
In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images.  ...  Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images.  ...  crop classification from multi-temporal images [3, 2, 10, 11, 1, 12] .  ... 
arXiv:2102.07280v2 fatcat:77mcjygmgzdjxbfc6iqjcn37za

End-to-End CNN-CRFs for Multi-date Crop Classification Using Multitemporal Remote Sensing Image Sequences

Laura Elena Cue La Rosa, Dário Augusto Borges Oliveira, Raul Queiroz Feitosa
2021 International Conference on Information and Knowledge Management  
In this context, deep learning methods such as convolutional neural networks (CNNs) proved to be an appealing alternative for feature learning in the context of remote sensing image classification.  ...  Experiments are presented for an agricultural region in Brazil using multi-temporal SAR images sequences.  ...  Acknowledgments We gratefully acknowledge the financial support offered by the Brazilian National Council for Scientific and Technological Development (CNPq) and the Foundation for Support of Research  ... 
dblp:conf/cikm/RosaOF21 fatcat:gkgrdgm22bfvhpqbvnyonybiim

Spatio-temporal Crop Classification On Volumetric Data [article]

Muhammad Usman Qadeer, Salar Saeed, Murtaza Taj, Abubakr Muhammad
2021 arXiv   pre-print
Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest.  ...  In this work, we present a novel CNN based architecture for large-area crop classification. Our methodology combines both spatio-temporal analysis via 3D CNN as well as temporal analysis via 1D CNN.  ...  The 3D convolutional neural networks are widely considered for applications related to video, medical imaging, and remote sensing [26] .  ... 
arXiv:2103.10050v1 fatcat:j2zcwrlchvgobf2v5pu6ngaoiy

Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network [article]

Rui Li, Shunyi Zheng, Chenxi Duan, Ce Zhang
2020 arXiv   pre-print
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.  ...  ` Abstract-Convolutional neural network (CNN) is an effective method to extract information from remote sensing images for land cover classification.  ...  Index Terms-spatio-temporal remote sensing images, multiscale fully convolutional network, land cover classification I.  ... 
arXiv:2008.00168v2 fatcat:zwllbq2hpvg6pimhkhlk6gi7zu

Exploiting Hierarchical Features for Crop Yield Prediction based on 3D Convolutional Neural Networks and Multi-kernel Gaussian Process

Mengjia Qiao, Xiaohui He, Xijie Cheng, Panle Li, Haotian Luo, Zhihui Tian, Hengliang Guo
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
1 Exploiting Hierarchical Features for Crop Yield Prediction based on 3D Convolutional Neural Networks and Multi-kernel  ...  Rosúa, “Multi-temporal relevant image features with multiple-kernel classification,” IEEE Trans- imaging using an unmanned aerial vehicle for monitoring  ... 
doi:10.1109/jstars.2021.3073149 fatcat:atyweubmxjekjivs7ecruyzqru


K. Meshkini, F. Bovolo, L. Bruzzone
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Since there are only a few pretrained 3D CNNs available that are not suitable for remote sensing CD analysis, the proposed approach starts with a pretrained 2D CNN architecture trained on remote sensing  ...  Over recent decades, Change Detection (CD) has been intensively investigated due to the availability of High Resolution (HR) multi-spectral multi-temporal remote sensing images.  ...  The main idea is to combine convolutional neural network (CNN) and well-established RNN for remote sensing applications.  ... 
doi:10.5194/isprs-archives-xliii-b3-2022-143-2022 fatcat:h36xucctvbaeppgehtmt36trtm


G. S. Phartiyal, D. Singh
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
</strong> Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task.  ...  In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical  ...  Recently, deep neural networks (DNNs) are making its mark as powerful tool for remote sensing applications.  ... 
doi:10.5194/isprs-archives-xlii-5-675-2018 fatcat:m43xaytj7bbtrnuxtlwxohirjy

Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

Chunhua Liao, Jinfei Wang, Qinghua Xie, Ayman Al Baz, Xiaodong Huang, Jiali Shang, Yongjun He
2020 Remote Sensing  
In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification.  ...  information of the crops, which is effective for crop classification in areas with frequent cloud interference.  ...  ., Bo Shan, Minfeng Xing and Yang Song for helping with the field data collecting. The authors also would like to thank the Centre National D'Etudes Spatiales (CNES) for providing VENµS data.  ... 
doi:10.3390/rs12050832 fatcat:czvr67re5fd5dofy4gmpxlg4pe

Satellite Computer Vision mit Keras und Tensorflow - Best practices und beispiele aus der Forschung

Jens Leitloff, Felix M. Riese
2019 Zenodo  
Remote Sensing For example: • Segmentation of high-resolution satellite images • Edge detection of crop fields • Machine Learning for the classification and regression of environmental variables  ...  Learning 1D patterns with CNNs) • 3D convolution over spatial & temporal dimension ○ temporal dimension → learn from temporal changes, e.g. crop Learning sequences 39 • Temporal changes → learning  ... 
doi:10.5281/zenodo.4056745 fatcat:l3xnsvu5hnapfohkaafolkvqkm

Spatial-Temporal Neural Network for Rice Field Classification from SAR Images

Yang-Lang Chang, Tan-Hsu Tan, Tsung-Hau Chen, Joon Huang Chuah, Lena Chang, Meng-Che Wu, Narendra Babu Tatini, Shang-Chih Ma, Mohammad Alkhaleefah
2022 Remote Sensing  
This research proposes a novel spatial-temporal neural network called a convolutional long short-term memory rice field classifier (ConvLSTM-RFC) for rice field classification from Sentinel-1A SAR images  ...  To improve the accuracy of agricultural and food surveys, AFA uses remote sensing technology to conduct surveys on the planting area of agricultural crops.  ...  Another recent work [42] proposed a 3D convolutional neural network (3D CNN) for rice crop yield estimation from Sentinel-2 images in Nepal.  ... 
doi:10.3390/rs14081929 fatcat:p7salfy7knd2xjt5yp7pzl5d44

Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network

Quanlong Feng, Jianyu Yang, Yiming Liu, Cong Ou, Dehai Zhu, Bowen Niu, Jiantao Liu, Baoguo Li
2020 Remote Sensing  
In this study, an attention-based recurrent convolutional neural network (ARCNN) has been proposed for accurate vegetable mapping from multi-temporal UAV red-green-blue (RGB) imagery.  ...  Vegetable mapping from remote sensing imagery is important for precision agricultural activities such as automated pesticide spraying.  ...  We would also like to thank Beijing IRIS Remote Sensing Technology Limited, Inc. for their help in preprocessing UAV raw data. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12101668 fatcat:fukpk5e5qzbcdjowogo2a5bb44

Crop Yield Prediction Techniques using Remote Sensing Data

2020 International Journal of Engineering and Advanced Technology  
The paper present a literature survey of various stastical method, empirical models,artificial neural network and machine learning regression techniques which are used with the data provided by the satellites  ...  Prediction of crop yield will be very useful for the government to make food policies, market price, import and export policies and proper warehousing well in time.  ...  features from videos, use of CNN for crop classification by using multi-temporal and multi-spatial remote sensing images is very emergent [30, 31] .  ... 
doi:10.35940/ijeat.c6217.029320 fatcat:omohesb5p5aido2rxp4c3chicm

Satellite data is for everyone: insights into modern remote sensing research with open data and Python

Jens Leitloff, Felix M. Riese
2018 Zenodo  
Finally, we give a quick overview about the current research topics including recurrent neural networks for spatio-temporal land-use classification and further applications of multi- and hyperspectral  ...  Machine learning techniques like convolutional neural networks (CNN) are able to learn the link between the satellite image (spectrum) and the ground truth (land use class).  ...  2D convolution over spatial dimension → this talk • 3D convolution over spectral & spatial dimensionMaking use of multi-temporal data → e.g. annual crop • Recurrent neural networks → Rußwurm, Körner (  ... 
doi:10.5281/zenodo.4056517 fatcat:hgedvoxxvzdgvcoeegjoyttifi

Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

Petteri Nevavuori, Nathaniel Narra, Petri Linna, Tarmo Lipping
2020 Remote Sensing  
Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures  ...  Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications.  ...  Acknowledgments: We would like to thank Tampere University for providing the computational resources and MIKÄ DATA project for providing us with the data.  ... 
doi:10.3390/rs12234000 fatcat:ndjg2ew7arhwzh23tebo4d4xpq
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