Constrained Nonnegative Matrix Factorization for Hyperspectral Change Detection

ERTÜRK A.

2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020, Tunis, Tunus, 9 - 11 Mart 2020, ss.49-52 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/m2garss47143.2020.9105146
  • Basıldığı Şehir: Tunis
  • Basıldığı Ülke: Tunus
  • Sayfa Sayıları: ss.49-52
  • Anahtar Kelimeler: Change detection, hyperspectral, multitemporal, nonnegative matrix factorization, unmixing
  • Kocaeli Üniversitesi Adresli: Evet

Özet

© 2020 IEEE.This paper presents an unmixing based change detection (UBCD) approach based on constrained nonnegative matrix factorization (NMF) for hyperspectral images. UBCD provides not only multi-output change detection, but also subpixel level information about the nature of the changes that occur in the scene. The proposed method utilizes constrained NMF with the sparsity constraint for the abundances and the minimum volume constraint for the endmembers, reducing the solution space for the matrix factorization and resulting in enhanced unmixing and change detection performance. The change detection output is obtained in terms of the temporal abundance matrix differences for each endmember. The proposed method is evaluated on synthetic and real multitemporal datasets.