Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data-Reference-Cited by-同舟云学术

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Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

Published:2017-08-31 Issue:23 Volume:38 Page:7138-7160
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Khosravi Iman¹,Safari Abdolreza¹,Homayouni Saeid²,McNairn Heather³

Affiliation:

1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2. Department of Geography, Environment, and Geomatics, University of Ottawa, Ottawa, Canada

3. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2017.1372863

Reference64 articles.

1. Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery

2. A study of land cover classification using polarimetric SAR parameters

3. Comparison of polarimetric SAR observables in terms of classification performance

4. Improving PolSAR Land Cover Classification With Radiometric Correction of the Coherency Matrix

5. Supervised feature selection for polarimetric SAR classification

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1. Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review;Journal of Imaging;2024-01-05

2. Enhancing Crop Classification Accuracy through Synthetic SAR-Optical Data Generation Using Deep Learning;ISPRS International Journal of Geo-Information;2023-11-02

3. Land Cover Mapping and Change Analysis Using Optimized Random Forest Classifier Incorporating Fusion of Texture and Gabor Features;SN Computer Science;2023-09-08

4. The combination of the NDBI and machine learning algorithms to classification the development of urban areas in Surabaya uses Landsat 8 Imagery.;2023 International Electronics Symposium (IES);2023-08-08

5. An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data;Remote Sensing;2023-02-21

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