Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması

Year 2022, Volume: 22 Issue: 6, 1368 - 1379, 28.12.2022

Eren Can Seyrek Murat Uysal

https://doi.org/10.35414/akufemubid.1177912

Abstract

Hiperspektral Görüntüler (HSG), sağladığı yüksek spektral çözünürlük sayesinde birçok alanda kullanım alanına sahiptir. HSG’lerin sınıflandırılması, görüntülerin yüksek spektral çözünürlüğü sebebiyle zorlayıcı bir süreçtir. Bu bağlamda HSG’lerin sınıflandırılmasında birçok Makine Öğrenme (MÖ) algoritmasının performansı araştırılmıştır. Özellikle Derin Öğrenmenin alt dallarından biri olan Evrişimli Sinir Ağları (ESA) tabanlı birçok ağ mimarisi HSG’lerin sınıflandırılması için özel olarak geliştirilmiştir. Hiperspektral görüntüleme sistemlerinin (HGS) yüksek maliyetleri sebebiyle veri setlerinin elde edilmesi zordur. Son yıllarda insanlı ve insansız hava araçları (İHA) için geliştirilen yeni nesil hiperspektral görüntüleme sistemlerinin maliyetleri giderek düşmekte olup yüksek mekânsal çözünürlüklü ve uygun maliyetli HSG elde edilmesi mümkün hale gelmiştir. Bu çalışmada çeşitli platformlardan elde edilmiş farklı spektral ve uzamsal çözünürlükteki HSG’lerin sınıflandırılmasında çeşitli MÖ algoritmalarının performansının incelenmesi amaçlanmıştır. Bu kapsamda uydu tabanlı HyRANK Loukia, hava aracı tabanlı Chikusei İHA tabanlı WHU-Hi HanChuan isimli görüntüler Destek Vektör Makineleri, Rastgele Orman ve ESA algoritmaları kullanılarak sınıflandırılmıştır. Sınıflandırma performansları incelendiğinde en yüksek genel doğruluk değerleri veri setleri için sırasıyla %87,78, %99,82 ve %96,89 olarak ESA tarafından elde edildiği görülmüştür.

Keywords

Hiperspektral görüntü, Görüntü sınıflandırma, Evrişimli sinir ağları, Destek vektör makineleri, Rastgele orman

Supporting Institution

Afyon Kocatepe Üniversitesi Bilimsel Araştırma Projesi Koordinatörlüğü

Project Number

20.FEN.BİL.12

Thanks

Yazarlar HyRANK veri setini sağlayan ISPRS Komisyon III WG III/4'e; Chikusei Hyperspectral Data veri setini sağlayan Tokyo Üniversitesine; WHU-Hi veri setini sağlayan RSIDEA akademik araştırma grubuna, LIESMARS’a ve Wuhan Üniversitesine teşekkür ederler. Bu çalışma AKÜ BAPK tarafından 20.FEN.BİL.12 numaralı proje kapsamında desteklenmiştir.

Comparison of Performances of CNN, SVM and RF Algorithms in Classification of Hyperspectral Images Obtained from Different Platforms

Abstract

Hyperspectral Images (HSI) are employed in many fields, owing to the high spectral resolution that offer. Classification of HSIs is a challenging process due to the high spectral resolution of the images. In this regard, the performance of various Machine Learning (ML) algorithms in the classification of HSGs have been investigated. Especially, Convolutional Neural Networks (CNN) architectures, have been specially developed for the classification of HSIs. Due to the high cost of hyperspectral imaging instruments, obtaining HSI datasets is challenging. In recent years, the costs of new generation hyperspectral imaging systems developed for manned and unmanned aerial vehicles (UAV) have been decreasing, and it has become possible to obtain high spatial resolution and cost-effective HSIs. In this study, it is aimed to examine the performance of various ML algorithms in the classification of HSIs with different spectral and spatial resolutions obtained from various platforms. In this context, satellite-based HyRANK Loukia, manned aircraft-based Chikusei and UAV-based WHU-Hi HanChuan images were classified using Support Vector Machines, Random Forest and CNN algorithms. When the classification performances were examined, it was seen that the highest overall accuracy values were obtained by CNN as 87.78%, 99.82% and 96.89% for the data sets, respectively.

Keywords

Hyperspectral image, Image classification, Convolutional neural networks, Support vector machine, Random forest

Project Number

20.FEN.BİL.12

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