Hyperspectral Image Classification Using MiniVGGNet

Öz

Hyperspectral image classification is widely used in the analysis of remote sensing images. Recently, deep learning has been seen as the most effective method for hyperspectral image classification. Especially, Convolutional neural networks (CNN) are getting more and more attention in this field. CNN provides automated approaches that can learn more abstract features of hyperspectral images from spectral, spatial or spectral-spatial fields. In this study, a 3D CNN based MiniVGGNet network is proposed to take full advantage of the relationships between hyperspectral features and to increase the classification accuracy. With 3D CNN, spectral-spatial features are extracted simultaneously. With MiniVGGNet, the number of trainable parameters is reduced and the training time is shortened. In addition, principal component analysis (PCA) is used as a preprocessing method to reduce the computational complexity caused by the high dimensionality of hyperspectral images. In order to test the performance of the proposed method, applications were performed on remote sensing datasets of Indian Pines, University of Pavia and Salinas. The results were compared with different deep learning-based methods. Better classification performance is obtained by using the proposed method for hyperspectral image classification.

Anahtar Kelimeler

• Principal component analysis
• MiniVGGNet
• Hyperspectral image classification
• 3D CNN.

MiniVGGNet Kullanılarak Hiperspektral Görüntü Sınıflandırma

Öz

Hiperspektral görüntü sınıflandırma uzaktan algılanan görüntülerin analizinde yaygın olarak kullanılmaktadır. Son zamanlarda, derin öğrenme hiperspektral görüntü sınıflandırmasında en etkili yöntem olarak görülmektedir. Özellikle evrişimsel sinir ağları (ESA) bu alanda giderek daha fazla ilgi görmektedir. ESA, spektral, uzamsal veya spektral-uzamsal alanlardan hiperspektral görüntülerin daha soyut özelliklerini öğrenebilen otomatik yaklaşımlar sağlamaktadır. Bu çalışma kapsamında, hiperspektral özellikler arasındaki ilişkilerden tam olarak yararlanmak ve sınıflandırma doğruluğunu arttırmak için 3B ESA tabanlı MiniVGGNet ağı önerilmektedir. 3B ESA ile spektral-uzamsal özellikler eş zamanlı olarak çıkarılmaktadır. MiniVGGNet ile de eğitilebilir parametre sayısı azaltılmakta ve eğitim süresi kısaltılmaktadır. Ayrıca, hiperspektral görüntülerin yüksek boyutluluğundan kaynaklanan hesaplama karmaşıklığını azaltmak için ön işleme yöntemi olarak temel bileşen analizi kullanılmaktadır. Önerilen yöntemin performansını test etmek için Indian Pines, Pavia Üniversitesi ve Salinas uzaktan algılama veri kümeleri üzerinde uygulamalar gerçekleştirilmiştir. Sonuçlar, farklı derin öğrenme tabanlı yöntemlerle karşılaştırılmıştır. Hiperspektral görüntü sınıflandırması için önerilen yöntem kullanılarak daha iyi sınıflandırma performansı elde edilmiştir.

Anahtar Kelimeler

• Temel bileşen analizi
• MiniVGGNet
• Hiperspektral görüntü sınıflandırma
• 3B ESA.

Kaynakça

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