A Survey of Particle Swarm Optimization and Random Forest based Land Cover Classification

被引：0

作者：

Shahana, K. ^{[1
]}

Ghosh, Subhajit ^{[1
]}

Jeganathan, C. ^{[2
]}

机构：

[1] Galgotias Univ, Greater Noida, India

[2] Birla Inst Technol, Ranchi, Bihar, India

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA) | 2016年

关键词：

Random Forest; Particle Swarm Optimization; Machine Learning; Classification; Support Vector Machine; Ant Colony Optimization; SEGMENTATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The main objective of the survey is to Study the emerging classifiers like Random Forest (RF) and Particle Swarm Optimization (PSO) and its application to Satellite Imageries to achieve enhanced and highly accurate Land Cover Classification Model. RF is an ensemble type voting based machine learning algorithm. RF algorithm considers single pixels for Classification instead of sets of pixels as in the case of object-oriented algorithms. Particle Swarm Optimization is an advanced but simple search strategy for optimizing complex numerical functions based on group response of bird flocking. It is a population based process of Swarm Intelligence. This paper is a literature survey that examines the performances of PSO and RF algorithm.

引用

页码：241 / 245

页数：5

共 50 条

[31] Land Cover Remote Sensing Classification Method of Alpine Wetland Region Based on Random Forest Algorithm
Hou M.
Yin J.
Ge J.
Li Y.
Feng Q.
Liang T.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2020, 51 (07): : 220 - 227
[32] Chinese Text Classification Based on Particle Swarm Optimization
Luo Xin
PROCEEDINGS OF THE 2015 4TH NATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING ( NCEECE 2015), 2016, 47 : 53 - 58
[33] Classification rule mining based on particle swarm optimization
Wang, Ziqiang
Sun, Xia
Zhang, Dexian
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2006, 4062 : 436 - 441
[34] Survey on Particle Swarm Optimization Based Clustering Analysis
Mangat, Veenu
SWARM AND EVOLUTIONARY COMPUTATION, 2012, 7269 : 301 - 309
[35] Incorporating Random Forest Trees with Particle Swarm Optimization for Automatic Image Annotation
Sami, Mohamed
Hassanien, Aboul Ella
El-Bendary, Nashwa
Berwick, Robert C.
2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2012, : 763 - 769
[36] Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization
Luo, Weiping
Yuan, Dajun
Jin, Dalong
Lu, Ping
Chen, Jian
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2021, 128 (01): : 109 - 127
[37] PREDICTION OF SILICON CONTENT IN HOT METAL BASED ON GOLDEN SINE PARTICLE SWARM OPTIMIZATION AND RANDOM FOREST
Hu, CH.
Yang, K.
METALURGIJA, 2022, 61 (02): : 325 - 328
[38] The Random Forest Approach for Land Cover Mapping
Aonpong, Panyanat
Kasetkasem, Teerasit
Rakwatin, Preesan
Kumazawa, Itsuo
Chanwimaluang, Thitiporn
7TH INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION TECHNOLOGY FOR EMBEDDED SYSTEMS 2016 (IC-ICTES 2016), 2016, : 1 - 6
[39] Particle swarm classification: A survey and positioning
Nouaouria, Nabila
Boukadoum, Mounir
Proulx, Robert
PATTERN RECOGNITION, 2013, 46 (07) : 2028 - 2044
[40] Land Use and Land Cover Area Estimates From Class Membership Probability of a Random Forest Classification
Sales, Marcio H. R.
de Bruin, Sytze
Souza, Carlos, Jr.
Herold, Martin
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60

← 1 2 3 4 5 →