IDA: Improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images

作者：

Highlights：

• A noverl feature extraction method to reduce HSI dimensions, called IDA is proposed.

• IDA is applied to improve data correlation and distribution and classes variance.

• Study the impact of feature extraction and preproccesing on the HSI classification.

• Using different datasets with well-known exisiting methods to test the IDA.

摘要

•A noverl feature extraction method to reduce HSI dimensions, called IDA is proposed.•IDA is applied to improve data correlation and distribution and classes variance.•Study the impact of feature extraction and preproccesing on the HSI classification.•Using different datasets with well-known exisiting methods to test the IDA.

论文关键词：Feature reduction,Hyperspectral image,Classification,Feature fusion,Feature extraction,Dimensionality reduction

论文评审过程：Received 3 August 2022, Revised 16 September 2022, Accepted 4 October 2022, Available online 8 October 2022, Version of Record 13 October 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109096