Image feature extraction by dynamic neural filtering and phase-only joint transform correlation

Bal, Abdullah

doi:10.1016/j.optlastec.2005.05.020

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Therefore, dimensionality reduction is crucial for hyperspectral image classification. The commonly-used dimensionality reduction methods can be divided into two categories: feature selection [3] and feature extraction [4]. With feature extraction methods, the high-dimensional feature space is mapped to a low-dimensional space by linear or nonlinear transformations.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm of Improved Optimum Index Factor Band Selection from Hyperspectral Remote Sensing Image

Hu¹,

Wang²,

Xing³

2018

dtcse

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Abstract. Hyperspectral remote sensing sensors can capture hundreds of narrow contiguous bands and provide plenty of valuable information. Due to the high-dimension characteristics of hyperspectral data, band selection plays an important role in the field of Hyperspectral Image (HSI) classification. In this paper, a HSI classification method based on Improved Optimum Index Factor (IOIF) band selection is proposed. First, we review the standard OIF and its shortcoming for processing huge bands. Then we introduce the proposed feature selection method based on IOIF. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach can select those bands with more discriminative information rapidly and improve the classification accuracy effectively.

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Section: Introductionmentioning

confidence: 99%