4CF-Net: New 3D convolutional neural network for spectral spatial classification of hyperspectral remote sensing images

Huseyin F., HANBAY D.

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.37, sa.1, ss.439-453, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.17341/gazimmfd.901291
  • Dergi Adı: JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.439-453
  • Anahtar Kelimeler: Hyperspectral image classification, deep learning, 3D convolutional neural network, remote sensing
  • İnönü Üniversitesi Adresli: Evet

Özet

Hyperspectral images (HSI) are contiguous band images commonly used in remote sensing. Deep learning (DL) is an effective method used in HSI classification. Convolutional neural networks (CNN) are one of the DL methods used in HSI classification. It provides automated approaches that can learn abstract features of HSIs from spectral-spatial fields. The high dimensionality of the HSIs increases the computational complexity. Therefore, most of the developed CNN models perform dimensionality reduction as a preprocessing step. Another problem in HSI classification is that spectral-spatial features must be considered in order to obtain accurate results. Because, HSI classification performance is highly dependent on spectral spatial information. In this study, a new 3D CNN model is proposed for HSI classification. The proposed method provides an effective method to simultaneously extract spectral-spatial features in HSIs. The network uses the 3D hyperspectral cube at the input. Principal component analysis is used to eliminate the dimensional redundancy in the hyperspectral cube. Then, using neighborhood extraction, spectral-spatial features are extracted effectively. The proposed method has been tested with 4 datasets. The application results were compared with 7 different DL-based methods and it was seen that our 4CF-Net method showed better classification performance.