Evaluation of five feature selection methods for remote sensing data

2001 ◽  
Author(s):  
Aniati Murni ◽  
S. Mulyono ◽  
Dina Chahyati
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Remote Sensing Data ◽  
Selection Methods ◽  
Sensing Data

scholarly journals Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S.

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2003
Author(s):  
Ling Zhang ◽  
Zixuan Zhang ◽  
Zhaohui Xue ◽  
Hao Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Selection ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Feature Subset ◽  
Selection Methods ◽  
Sensing Data

Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.

Feature selection for remote-sensing data classification

1994 ◽  
Author(s):  
Sebastiano B. Serpico ◽  
Paolo Pellegretti ◽  
Lorenzo Bruzzone
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Sensing Data ◽  
Selection For
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