Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques-Reference-Cited by-同舟云学术

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Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques

Published:2020-02-25 Issue:sup1 Volume:53 Page:126-140
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Natteshan N.V.S¹,Suresh Kumar N.¹

Affiliation:

1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2020.1727777

Reference34 articles.

1. Object based image analysis for remote sensing

2. Deep Learning-Based Classification of Hyperspectral Data

3. A Wavelet Decomposition and Polynomial Fitting-Based Method for the Estimation of Time-Varying Residual Motion Error in Airborne Interferometric SAR

4. A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

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1. A Multispectral Remote Sensing Crop Segmentation Method Based on Segment Anything Model Using Multistage Adaptation Fine-Tuning;IEEE Transactions on Geoscience and Remote Sensing;2024

2. Fast Dynamic Time Warping and Hierarchical Clustering with Multi-Spectral and Synthetic Aperture Radar Temporal Analysis for Unsupervised Winter Food Crop Mapping;2024

3. A Hybrid Multistage Model Based on YOLO and Modified Inception Network for Rice Leaf Disease Analysis;Arabian Journal for Science and Engineering;2023-11-16

4. Leveraging involution and convolution in an explainable building damage detection framework;European Journal of Remote Sensing;2023-09-05

5. Using a Novel Green Index to Support Ecosystem Services in a Megacity;Forests;2023-08-24

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