Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy

doi:10.23940/ijpe.24.05.p1.263270

Abstract

Abstract: Deep learning techniques have transformed image processing by providing powerful tools for extracting detailed patterns and features from large amounts of data. This paper provides a unique hybrid methodology for hyperspectral image (HSI) analysis that integrates Random Forest (RF) feature selection with Convolutional Neural Networks (CNNs) for classification in the domain of HSI classification. The study leverages CNNs' inherent automatic hierarchical feature extraction ability and RF's effectiveness in recognizing and conserving essential features. This study thoroughly validated the proposed approach using the Indian Pines dataset, a prominent HSI dataset of dimensions 145x145 pixels and 200 spectral bands collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The combination of RF-driven dimensionality reduction and CNN-based classification yields a robust model with an accuracy rate of 99.35%, demonstrating its efficacy in categorizing HSIs with varying object scales.

Key words: image detection, dimensionality reduction, random forest, hyperspectral images (HSI)

Sanjay M, Deepashree P. Vaideeswar, Kalapraveen Bagadi, Visalakshi Annepu, and Beebi Naseeba. Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy [J]. Int J Performability Eng, 2024, 20(5): 263-270.

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