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3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images
2018
Remote Sensing
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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]
2021
arXiv
pre-print
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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
2021
International Conference on Information and Knowledge Management
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
A 3D CNN APPROACH FOR CHANGE DETECTION IN HR SATELLITE IMAGE TIME SERIES BASED ON A PRETRAINED 2D CNN
2022
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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
COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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</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
2020
Remote Sensing
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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
2019
Zenodo
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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
2022
Remote Sensing
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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
2020
Remote Sensing
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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
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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
2018
Zenodo
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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
2020
Remote Sensing
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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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