Simulation of hyperspectral image with existing Sentinel and AVIRIS data using distance functions

Peddinti, Veerendra Satya Sylesh; Mandla, Venkata Ravibabu; Shashi, M.; Kancherla, Suresh

doi:10.1007/s12517-021-08136-6

Cited by 1 publication

(1 citation statement)

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“…Experts in the field have carried out research from various directions on the negative effects of spectrally similar objects in hyperspectral images. In [1], VSS Peddinti et al proposed a method for hyperspectral image simulation of similar spectra using a distance function. This study concentrates on obtaining similar spectra using distance functions.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yuan

et al. 2022

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%