Hyperspectral Imaging: Methods and Applications

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21 pages, 2385 KiB

Open AccessArticle

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

by Raju Anand, Sathishkumar Samiaappan, Shanmugham Veni, Ethan Worch and Meilun Zhou

J. Imaging 2022, 8(5), 126; https://doi.org/10.3390/jimaging8050126 - 27 Apr 2022

Cited by 14 | Viewed by 2856

Abstract

In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy. Full article

(This article belongs to the Topic Hyperspectral Imaging: Methods and Applications)

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25 pages, 7553 KiB

Open AccessArticle

Hyperspectral Band Selections for Enhancing the Discrimination of Difficult Targets Using Local Band Index and Particle Swarm Optimization

by Hanwen Wang, Changxiang Yan, Jing Yuan and Qipeng Lu

Appl. Sci. 2022, 12(8), 3899; https://doi.org/10.3390/app12083899 - 12 Apr 2022

Cited by 5 | Viewed by 2074

Abstract

Due to their similar color and material variability, some ground objects have similar characteristics and overlap in some bands. This leads to a drop in the classification accuracy of hyperspectral images. To address this problem, we simulated hyperspectral images of vegetation and objects with similar colors by mixed pixel calculation to test the classification performance of the dimensionality reduction method for samples with close spectra. In addition, we proposed a novel wavelength selection algorithm called the LBI-BPSO (Binary Particle Swarm Optimization with Local Band Index), which combines the information amount and inter-class separability. The novelty of this study is in its proposal of an improvement of IOIF using inter-class distance. Based on the calculation of the information content by the local band index, the inter-class distance was introduced to measure the inter-class separability of ground objects, and a reasonable fitness function is proposed. It can obtain the wavelength combination of two DR criteria, which considers the larger amount of information and better sample separability. The classification performance of the simulation dataset is verified by comparing LBI-BPSO with Partitioned Relief-F, IOIF (Improved Optimum Index Factor) and GA-BPSO (Particle Swarm Optimization with a Genetic Algorithm). Under the conditions that the signal-to-noise ratio is 1000, compared with IOIF, the OA of LBI-BPSO improved by 2.90%, the AA improved by 2.75%, and the Kappa coefficient improved by 3.91%. LBI-BPSO also showed the best results in the analysis of different abundances and signal-to noise-ratios. The results show that the new wavelength selection algorithm LBI-BPSO, which combines the amount of information and inter-class separability, is more effective than IOIF and GA-BPSO in classifying objects with similar colors and effectively improves the classification accuracy. Full article

(This article belongs to the Topic Hyperspectral Imaging: Methods and Applications)

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12 pages, 3932 KiB

Open AccessArticle

Interference Spectral Imaging Based on Liquid Crystal Relaxation and Its Application in Optical Component Defect Detection

by Jiajia Yuan, Wei Fan, He Cheng, Dajie Huang and Tongyao Du

Appl. Sci. 2022, 12(2), 718; https://doi.org/10.3390/app12020718 - 12 Jan 2022

Cited by 1 | Viewed by 2359

Abstract

In this paper, we propose a fast interference spectral imaging system based on liquid crystal (LC) relaxation. The path delay of nematic LC during falling relaxation is used for the scanning of the optical path. Hyperspectral data can be obtained by Fourier transforming the data according to the path delay. The system can obtain two-dimensional spatial images of arbitrary wavelengths in the range of 300–1100 nm with a spectral resolution of 262 cm⁻¹. Compared with conventional Fourier transform spectroscopy, the system can easily collect and integrate all valid information within 20 s. Based on the LC, controlling the optical path difference between two orthogonally polarized beams can avoid mechanical movement. Finally, the potential for application in contactless and rapid non-destructive optical component defect inspection is demonstrated. Full article

(This article belongs to the Topic Hyperspectral Imaging: Methods and Applications)

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