A Comparative Study for Hyperspectral Data Classification with Deep Learning and Dimensionality Reduction Techniques

Yıl 2018, Cilt: 23 Sayı: 3, 73 - 90, 26.10.2018

Gizem Ortaç Gıyasettin Özcan

https://doi.org/10.17482/uumfd.435723

Öz

In recent years, hyperspectral imaging has been a
popular subject in the remote sensing community 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 to handle high-dimensional and highly correlated feature
spaces. However, traditional classifiers and dimensionality reduction methods
are difficult tasks in the spectral domain and cannot extract discriminative
features. Recently, deep convolutional neural networks are proposed to classify
hyperspectral images directly in the spectral domain. In this paper, we present
comparative study among traditional data reduction techniques and convolutional
neural network. The obtained results on hyperspectral image
data sets show that our proposed CNN architecture improves
the accuracy rates for classification performance, when compared to traditional
methods by increasing the classification accuracy rate by 3% and 6%. 

Anahtar Kelimeler

Hyperspectral Imaging, Deep Learning, Dimensionality Reduction, Classification, Convolutional Neural Networks

HİPERSPEKTRAL VERİLERİN SINIFLANDIRMASINDA DERİN ÖĞRENME VE BOYUT İNDİRGEME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Öz

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.

Anahtar Kelimeler

Hiperspektral Görüntüleme, Derin Öğrenme, Boyut Azaltma, Sınıflandırma, Konvolüsyonel Sinir Ağları, Konvolüsyonel Sinir Ağları

Kaynakça

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