Feature Extraction from Hyperspectral Image Using Decision Boundary Feature Extraction Technique

  • R. Venkatesan
  • S. PrabuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


Hyperspectral pix is captured from satellite TV for pc sensors, which incorporates airborne seen or infrared imaging spectrometer that provides distinct facts approximately spectral and temporal features to categorize the materials than the landscape records. Snapshots contain ridiculous and proper spectral data, and the development of property use/cowl class accuracy is projected from using such photographs. This type of techniques facilitates to enclose efficaciously that is useful to multispectral fact. The essential foundation is with the aim to the extent of educational facts situated while doing no longer parallel toward the augment of ambit of hyperspectral statistics. However, to develop methods to system hyperspectral photograph is quite tough and studies objective. In order to conquer these troubles, the usage is made of functions’ extraction with dimensionality reduction. Attribute origin is recognized toward exist a valuable method within each lowering compotation difficulty along with growing precision of hyperspectral picture category. In this paper, an easy and however a pretty influential characteristic removal technique based totally scheduled choice boundary characteristic extraction (DBFE) is proposed. Initially, the hyperspectral picture is partitioned keenly on obstacles of adjoining hyperspectral bands, the bands in every separation are concert by using averaging and that is solitary of the best decision strategies. Finally, the device offers functions with reduced number. Hyperspectral records set turned into are examined to demonstrate the presentation of the novel technique and also compared the present strategies of function extraction.


Hyperspectral imaging Features’ extraction Region information Dimensionality reduction Hyperspectral bands 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia

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