An efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation

Süha Tuna*, Evrim Korkmaz Özay, Burcu Tunga, Ercan Gürvit, M. Alper Tunga

*Bu çalışma için yazışmadan sorumlu yazar

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5 Atıf (Scopus)


Hyperspectral (HS) Imagery helps to capture information using specialized sensors to extract detailed data at numerous narrow wavelengths. Hyperspectral imaging provides both spatial and spectral characteristics of regions or objects for subsequent analysis. Unfortunately, various noise sources decrease the interpretability of these images as well as the correlation between neighbouring pixels, hence both reduce the classification performance. This study focuses on developing an ensemble algorithm that enables to denoise the spectral signals while decorrelating the spatio-spectral features concurrently. The developed method is called Wavelet High Dimensional Model (W-HDMR) and combines High Dimensional Model Representation (HDMR) with the Discrete Wavelet Transform (DWT). Through W-HDMR, denoised and decorrelated features are extracted from the HS cubes. HDMR decorrelates each dimension in HS data while DWT denoises the spectral signals. The classification performance of W-HDMR as a new feature extraction technique for HS images is assessed by exploiting a Support Vector Machines algorithm. The results indicate that the proposed W-HDMR method is an efficient feature extraction technique and is considered an adequate tool in the HS classification problem.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)6899-6920
Sayfa sayısı22
DergiInternational Journal of Remote Sensing
Basın numarası19-24
Yayın durumuYayınlandı - 2022

Bibliyografik not

Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.


This work was supported by Istanbul Technical University Scientific Research Projects Coordination Unit (ITU-BAP) with project grant number MAB-2021-43503. The authors dedicate this work to Professor Metin Demiralp who made substantial contributions to HDMR theory.

FinansörlerFinansör numarası
Istanbul Teknik Üniversitesi
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik ÜniversitesiMAB-2021-43503

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