LIDAR-AIDED TOTAL VARIATION REGULARIZED NONNEGATIVE TENSOR FACTORIZATION FOR HYPERSPECTRAL UNMIXING
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Hyperspectral unmixing (HU) is an important research field in hyperspectral image processing. In recent years, Nonnegative Tensor Factorization (NTF)-based methods have gained great importance in remote sensing imagery, especially hyperspectral unmixing, regardless of any information loss. Nevertheless, NTF has some disadvantages, such as signal-to-noise ratio (SNR) and noncovexity conditions. Mentioned problem can be solved by introducing some spatial regularizations. On the other hand, LiDAR data provides Digital Surface Model (DSM) information gives accurate elevation information about the observed scene. Moreover, total variation (TV)-based regularization provides piecewise smoothness and it preserve edge structure information in the abundance maps. However, this property could be inappropriate for pixels located in edges. LiDAR-DSM alleviates this problem by contributing neighboring objects pixels differently. In this paper, we proposed a simple yet efficient HU framework that incorporates LiDAR data with TV regularized matrix-vector NTF method (LiMVNTF-TV). Experimental studies are carried out on simulation data sets and demonstrate that the proposed framework can provide better abundance estimation maps. © 2021 IEEE.
Açıklama
The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)
2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 -- 12 July 2021 through 16 July 2021 -- Brussels -- 176845
2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 -- 12 July 2021 through 16 July 2021 -- Brussels -- 176845
Anahtar Kelimeler
ADMM; Data fusion; Hyperspectral (HS) image; Light detection; NTF; Ranging (LiDAR); Spectral unmixing; Total variation
Kaynak
International Geoscience and Remote Sensing Symposium (IGARSS)
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
2021-July
