Dimensionality Reduction for the Feature System in Classification of Hyperspectral Earth Remote Sensing Data by Use of Neural Networks

被引：0
作者：
V. I. Kozik
E. S. Nezhevenko
机构：
[1] Institute of Automation and Electrometry,
[2] Siberian Branch,undefined
[3] Russian Academy of Sciences,undefined
来源：
Optoelectronics, Instrumentation and Data Processing | 2022年 / 58卷
关键词：
Earth’s remote sensing; hyperspectral images; classification; neural networks; learning; genetic algorithm; feature number reduction;
D O I：
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学科分类号：
摘要：
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页码：1 / 7
页数：6
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共 14 条
  • [1] Craig R.(2002)Principal component analysis for hyperspectral image classification Surv. Land Inf. Syst. 62 115-122
  • [2] Jie S.(2019)Neural network classification of difficult-to-distinguish types of vegetation on the basis of hypespectral features Optoelectron., Instrum. Data Process. 55 263-270
  • [3] Nezhevenko E. S.(2021)Classification of hypespectral images using conventional neural networks Optoelectron., Instrum. Data Process. 57 123-131
  • [4] Kozik V. I.(2020)Selection of the informative feature system for crops classification using hyperspectral data Optoelectron., Instrum. Data Process. 56 431-439
  • [5] Nezhevenko E. S.(2012)A compact, high-speed, and low-cost hyperspectral imager Proc. SPIE 8266 82660Q-undefined
  • [6] Borzov S. M.(2013)A snapshot multispectral imager with integrated tiled filters and optical duplication Proc. SPIE 8613 861314-undefined
  • [7] Potaturkin O. I.(undefined)undefined undefined undefined undefined-undefined
  • [8] Tack N.(undefined)undefined undefined undefined undefined-undefined
  • [9] Lambrechts A.(undefined)undefined undefined undefined undefined-undefined
  • [10] Soussan S.(undefined)undefined undefined undefined undefined-undefined
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