Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques
Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promisin...
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Online Access: | http://dx.doi.org/10.1080/22797254.2020.1727777 |
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doaj-9bd2da0809fe4a92954d07d87a279c452020-11-25T03:13:22ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542020-06-0153S112614010.1080/22797254.2020.17277771727777Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniquesN.V.S Natteshan0N. Suresh Kumar1Vellore Institute of TechnologyVellore Institute of TechnologyCrop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques.http://dx.doi.org/10.1080/22797254.2020.1727777synthetic-aperture radar (sar)crop classificationcrop cultivationhigh pass filterhigh pass linear spatial (hls) filterbinary robust independent elementary features (brief)modified region growing (mrg)linear spatial filterconvolutional deep neural network (cdnn) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N.V.S Natteshan N. Suresh Kumar |
spellingShingle |
N.V.S Natteshan N. Suresh Kumar Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques European Journal of Remote Sensing synthetic-aperture radar (sar) crop classification crop cultivation high pass filter high pass linear spatial (hls) filter binary robust independent elementary features (brief) modified region growing (mrg) linear spatial filter convolutional deep neural network (cdnn) |
author_facet |
N.V.S Natteshan N. Suresh Kumar |
author_sort |
N.V.S Natteshan |
title |
Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques |
title_short |
Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques |
title_full |
Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques |
title_fullStr |
Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques |
title_full_unstemmed |
Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques |
title_sort |
effective sar image segmentation and classification of crop areas using mrg and cdnn techniques |
publisher |
Taylor & Francis Group |
series |
European Journal of Remote Sensing |
issn |
2279-7254 |
publishDate |
2020-06-01 |
description |
Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques. |
topic |
synthetic-aperture radar (sar) crop classification crop cultivation high pass filter high pass linear spatial (hls) filter binary robust independent elementary features (brief) modified region growing (mrg) linear spatial filter convolutional deep neural network (cdnn) |
url |
http://dx.doi.org/10.1080/22797254.2020.1727777 |
work_keys_str_mv |
AT nvsnatteshan effectivesarimagesegmentationandclassificationofcropareasusingmrgandcdnntechniques AT nsureshkumar effectivesarimagesegmentationandclassificationofcropareasusingmrgandcdnntechniques |
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