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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2018 / Cilt: 23 - Sayı: 3

A COMPARATIVE STUDY FOR HYPERSPECTRAL DATA CLASSIFICATION WITH DEEP LEARNING AND DIMENSIONALITY REDUCTION TECHNIQUES

Hiperspektral Verilerin Sınıflandırmasında Derin Öğrenme ve Boyut İndirgeme Tekniklerinin Karşılaştırılması

Gizem ORTAÇ
Gıyasettin ÖZCAN
Pages
73–90
Publication Date
2022-11-04
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The obtained results onhyperspectral image data sets show that our proposed CNN architecture improves the accuracy rates forclassification performance, when compared to traditional methods by increasing the classificationaccuracy rate by 3% and 6%.

📌 3 Key Takeaways

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Objective & Hypothesis:In recent years, hyperspectral imaging has been a popular subject in the remote sensingcommunity by providing a rich amount of information for each pixel about fields.
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Key Findings & Data:In general,dimensionality reduction techniques are utilized before classification in statistical pattern-classification tohandle high-dimensional and highly correlated feature spaces.
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Conclusion & Impact:The obtained results onhyperspectral image data sets show that our proposed CNN architecture improves the accuracy rates forclassification performance, when compared to traditional methods by increasing the classificationaccuracy rate by 3% and 6%.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In recent years, hyperspectral imaging has been a popular subject in the remote sensingcommunity by providing a rich amount of information for each pixel about fields. In general,dimensionality reduction techniques are utilized before classification in statistical pattern-classification tohandle high-dimensional and highly correlated feature spaces. However, traditional classifiers anddimensionality reduction methods are difficult tasks in the spectral domain and cannot extractdiscriminative features. Recently, deep convolutional neural networks are proposed to classifyhyperspectral images directly in the spectral domain. In this paper, we present comparative study amongtraditional data reduction techniques and convolutional neural network. The obtained results onhyperspectral image data sets show that our proposed CNN architecture improves the accuracy rates forclassification performance, when compared to traditional methods by increasing the classificationaccuracy rate by 3% and 6%.

Özet

Son yıllarda, hiperspektral görüntüleme yüzey pikselleri ile ilgili zengin miktarda bilgi sağlamasıyla uzaktan algılama alanında popüler bir konu olmuştur. Genel olarak, elde edilen yüksek boyutlu ve ilişkisel veriyi işlemek için, sınıflandırmadan önce boyut indirgeme teknikleri uygulanmaktadır. Bununla birlikte geleneksel sınıflandırıcılar ve boyut azaltma yöntemleri, spektral alanda hala zorlu bir işlemdir ve ayırt edici öznitelikler çıkarmaz. Son zamanlarda ise derin konvolüsyonel sinir ağları, hiperspektral görüntüleri doğrudan spektral alanda sınıflandırmak için geliştirilmiştir. Önerilen çalışmada, geleneksel sınıflandırma ve konvolüsyonel sinir ağları arasında karşılaştırmalı bir çalışma ve analiz yapılmıştır. Çeşitli hiperspektral görüntü verilerine dayanarak elde edilen sonuçlar, önerilen konvolüsyonel sinir ağının, geleneksel yöntemlerden %3 ve %6 oranında daha iyi bir sınıflandırma oranı sağladığını göstermiştir.

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Cite This Article

ORTAÇ, G., ÖZCAN, G. (2022). A COMPARATIVE STUDY FOR HYPERSPECTRAL DATA CLASSIFICATION WITH DEEP LEARNING AND DIMENSIONALITY REDUCTION TECHNIQUES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 23 - Sayı: 3, 73–90. https://ajindex.com/articles/643765

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References (2)

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