Classification of high resolution hyperspectral remote sensing data using deep neural networks

YÜKSEL M. E., Basturk N. S., Badem H., ÇALIŞKAN A., BAŞTÜRK A.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, cilt.34, sa.4, ss.2273-2285, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.3233/jifs-171307
  • Dergi Adı: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2273-2285
  • Anahtar Kelimeler: Hyperspectral remote sensing, deep learning, deep neural network, softmax classifier, stacked autoencoder, CLASSIFIERS, IMAGES
  • Erciyes Üniversitesi Adresli: Evet

Özet

The high resolution hyperspectral remote sensing data collected from urban and landscape areas have been extensively studied over the past decades. Recent applications pose an emerging need of analyzing the land cover types based on high resolution hyperspectral remote sensing data originating from remote sensory devices. Toward this goal, we propose a deep neural network (DNN) classifier in this paper. The DNN is constructed by combining a stacked autoencoder with desired numbers of autoencoders and a softmax classifier. Our experimental results based on the hyperspectral remote sensing data demonstrate that the presented DNN classifier can accurately distinguish different land covers including the mixed deciduous broadleaf natural forest and different land covers such as agriculture, roads, buildings, etc. We test the proposed method by using three different benchmark data sets. The proposed method showcases the huge potential of deep neural networks for hyperspectral data analysis.