Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas-Reference-Cited by-同舟云学术

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Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Published:2024-03-27 Issue:01 Volume:18 Page:
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:
Short-container-title:J. Appl. Rem. Sens.

Author:

Li Boxin¹,Ren Hong-e¹,Dong Pinliang²,Tian Jing³

Affiliation:

1. Northeast Forestry University, College of Computer and Control Engineering, Harbin, China

2. University of North Texas, Department of Geography and the Environment, Denton, Texas, United States

3. Heilongjiang Institute of Technology, College of Surveying and Mapping Engineering, Harbin, China

Publisher

SPIE-Intl Soc Optical Eng

Reference90 articles.

1. Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

2. Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies

3. Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

4. Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

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