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.

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 HYPERSPECTRAL IMAGE DATA MINING FOR BAND SELECTION IN AGRICULTURAL APPLICATIONS

2004 ◽  
Vol 47 (3) ◽  
pp. 895-907 ◽  
Author(s):  
S. G. Bajwa ◽  
P. Bajcsy ◽  
P. Groves ◽  
L. F. Tian
Keyword(s):  
Data Mining ◽  
Hyperspectral Image ◽  
Image Data ◽  
Band Selection ◽  
Hyperspectral Image Data ◽  
Agricultural Applications

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Johannes Jordan ◽  
Elli Angelopoulou ◽  
Andreas Maier
Keyword(s):  
Hyperspectral Image ◽  
Imaging Modality ◽  
High Capacity ◽  
Interactive Visualization ◽  
Image Data ◽  
Hyperspectral Image Analysis ◽  
Supervised Segmentation ◽  
Hyperspectral Image Data ◽  
Visualization Techniques ◽  
Application Specific

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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