New search algorithm for feature selection in high-dimensional remote sensing images

1998 ◽  
Author(s):  
Lorenzo Bruzzone ◽  
Sebastiano B. Serpico
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Search Algorithm ◽  
High Dimensional ◽  
Remote Sensing Images

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

scholarly journals Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3119
Author(s):  
Shuting Yang ◽  
Lingjia Gu ◽  
Xiaofeng Li ◽  
Tao Jiang ◽  
Ruizhi Ren
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Multi Temporal ◽  
Optimal Feature Selection ◽  
Crop Classification ◽  
Optimal Feature

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

Sign in / Sign up

Export Citation Format

Share Document