Evaluation Method for Band Selection Algorithms of Hyperspectral Image

2013 ◽  
Vol 684 ◽  
pp. 495-498
Author(s):  
Bai He Wang ◽  
Shi Qi Huang ◽  
Yi Hong Li
Keyword(s):  
Data Fusion ◽  
Dimension Reduction ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Amount Of Information ◽  
Algorithm Evaluation ◽  
Common Band ◽  
Selection Algorithms

Band selection algorithm is most important in data dimension reduction of hyperspectral image. There are many algorithms of band selection, but there are only few methods to do algorithm evaluation. A method is put forward in this paper to evaluate the band selection algorithm of hyperspectral image. The amount of information, brightness, image contrast and definition are defined as 4 indexes to measure deferent data fusion based on various band selection results. Based on the measurement, the evaluation of band selection algorithm is realized. In the paper, the evaluation method is used in the compare of 4 common band selection algorithms, the result of measurement is analyzed and the feasibility is verified.

Research on the Band Selection Algorithm Based on Noise Evaluation of Hyperspectral Image

2013 ◽  
Vol 718-720 ◽  
pp. 2142-2145
Author(s):  
Bai He Wang ◽  
Shi Qi Huang ◽  
Yi Hong Li
Keyword(s):  
Hyperspectral Image ◽  
Band Selection ◽  
Practical Application ◽  
Selection Algorithm ◽  
Sensing Technology ◽  
Local Standard ◽  
Noise Evaluation ◽  
Local Mean ◽  
Selection Algorithms ◽  
Better Than

Along with the development of hyperspectral remote sensing technology, band selection algorithm of hyperspectral image has become the research focus of hyperspectral application. A band selection algorithm based on noise evaluation of hyperspectral image and named as LMLSD is proposed in this paper. All the bands of hyperspectral image are ranked according to the SNR calculated based on local mean and local standard deviation of every band. Simulation results show that the performance of LMLSD is better than classical band selection algorithms of ABS and SDAA. The algorithm of LMLSD has validity and feasibility in practical application.

scholarly journals A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3456
Author(s):  
Chunlin He ◽  
Yong Zhang ◽  
Dunwei Gong
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Band Selection ◽  
Selection Algorithm ◽  
Evolutionary Clustering ◽  
Bee Colony ◽  
Hyperspectral Remote Sensing Images ◽  
Selection Algorithms ◽  
Grid Division

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM.

Band selection algorithm based on information entropy for hyperspectral image classification

2017 ◽  
Vol 11 (2) ◽  
pp. 026018 ◽  
Author(s):  
Li Xie ◽  
Guangyao Li ◽  
Lei Peng ◽  
Qiaochuan Chen ◽  
Yunlan Tan ◽  
...  
Keyword(s):  
Image Classification ◽  
Information Entropy ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Hyperspectral Image Classification

scholarly journals Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

2020 ◽  
Vol 12 (1) ◽  
pp. 425-442
Author(s):  
Ding Xiaohui ◽  
Li Huapeng ◽  
Li Yong ◽  
Yang Ji ◽  
Zhang Shuqing
Keyword(s):  
Remote Sensing ◽  
Swarm Intelligence ◽  
Classification Accuracy ◽  
Ant Colony Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Remote Sensing ◽  
Band Selection ◽  
Selection Algorithm ◽  
Comparative Performance ◽  
Selection Of

AbstractSwarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys–Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms’ performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA’s, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

scholarly journals Material Discrimination Algorithm Based on Hyperspectral Image

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jian Zhou ◽  
Zhuping Wang ◽  
Yingjie Jiao ◽  
Cong Nie
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Chromatic Aberration ◽  
Band Selection ◽  
Selection Algorithm ◽  
Adversarial Network ◽  
Hyperspectral Image Data ◽  
Material Discrimination ◽  
Initial Selection ◽  
Selection Of

Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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