Kernel Extreme Learning Machine for Hyperspectral Image Classification

Year 2023, Volume: 4 Issue: 2, 198 - 212, 28.09.2023

Mustafa Üstüner

https://doi.org/10.48123/rsgis.1237772

Abstract

Hyperspectral images have been actively used in many real-life applications, primarily in remote sensing, since it provides detailed and rich spectral information. There are several challenges in hyperspectral image classification because of the high spectral dimension and the complex structure and hence the advanced classification algorithms (ensemble learning and kernel-based methods) are usually preferred. In this study, the ability of kernel extreme learning machine for the classification of hyperspectral image was investigated and the classification performance was compared with two machine learning algorithms (support vector machines and random forest). “Indian Pines” hyperspectral dataset was used in this experimental study and there are 16 land cover classes in the study area. The principal component analysis was used for the dimensionality reduction and first 40 principal components were selected. The classification was performed for both original dataset and dimensionality reduction applied data. Highest classification accuracies were achieved by support vector machines with overall accuracies of 91,64% and 83,45% for the original dataset and dimensionality reduction applied data, respectively. Furthermore, the original data achieved higher performance with respect to dimensionality reduction applied data in terms of overall accuracy for all methods.

Keywords

Remote sensing, Extreme learning machine, Hyperspectral

Çekirdek Tabanlı Aşırı Öğrenme Makinesi ile Hiperspektral Görüntü Sınıflandırma

Abstract

Hiperspektral görüntüler, zengin spektral bilgi içerdiklerinden dolayı uzaktan algılama başta olmak üzere birçok alanda etkin bir şekilde kullanılmaktadır. Yüksek spektral boyutu ve karmaşık yapılarından dolayı, hiperspektral görüntülerin sınıflandırılmasında bazı sıkıntılar yaşanmaktadır ve bu nedenle sınıflandırma işlemlerinde ileri düzey algoritmalar (topluluk öğrenme algoritmaları, çekirdek tabanlı yöntemler vb.) tercih edilmektedir. Bu çalışma kapsamında, çekirdek tabanlı aşırı öğrenme makinesinin (ÇAÖM) hiperspektral görüntü sınıflandırmadaki kabiliyeti araştırılmış ve sınıflandırma performansı, iki farklı makine öğrenme algoritması (destek vektör makineleri ve rastgele orman) ile karşılaştırılmıştır. Çalışma kapsamında “Indian Pines” hiperspektral veri seti kullanılmıştır ve çalışma alanında 16 adet arazi örtüsü sınıfı bulunmaktadır. Boyut indirgeme amacıyla veriye temel bileşenler analizi yöntemi uygulanmıştır. Sınıflandırma işlemi hem orijinal hiperspektral verisine hem de temel bileşenler analizi ile boyutu indirgenmiş veriye uygulanmıştır. Boyut indirgeme işlemi sonucunda ilk 40 temel bileşen bant olarak seçilmiştir. En yüksek sınıflandırma doğrulukları hem orijinal veri seti için (%91,64) hem de boyutu indirgenmiş veri seti için (%83,45) DVM yöntemi ile elde edilmiştir. Ayrıca, orijinal veri setinin sınıflandırılması ile elde edilen doğrulukların boyutu indirgenmiş verinin sınıflandırması ile elde edilen doğruluklardan daha yüksek olduğu tespit edilmiştir.

Keywords

Uzaktan algılama, Aşırı öğrenme makinesi, Hiperspektral

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