LiDAR-Tabanlı toplam değişinti kısıtlı negatif-olmayan tensör faktörizasyonu ile hiperspektral karışım giderimi

Year 2023, Volume: 29 Issue: 1, 1 - 9, 28.02.2023

Kubilay Ataş Atakan Kaya Sevcan Kahraman

Abstract

Spektral karışım giderimi hiperspektral görüntülemenin temel araştırma alanlarından birisidir. Son yıllarda Negatif-olmayan Tensör Faktörizasyonuna dayalı yaklaşımlar, bilgi kaybına uğratmadığı ve hiperspektral görüntüleri daha iyi temsil edebildiği için uzaktan algılamada büyük bir önem kazanmıştır. Toplam Değişinti yaklaşımı ise, parçalı pürüzsüzlüğü sağlarken kenar bilgisini de korumaktadır. Öte yandan, kızılötesi algılayıcısı gözlemlenen sahne hakkında yükseklik bilgisini veren Dijital Yüzey Modeli verisini sağlamaktadır. Bu çalışmada, LiDAR Dijital Yüzey Modeli bilgisiyle Toplam Değişinti kısıtı birleştirilerek hiperspektral görüntülerin uzamsal çözünürlüğünü artırmak için tensör faktörizasyonuna dayalı karışım giderimi gerçekleştirilmiştir. Deneysel çalışmalar simülasyon ve gerçek veri setleri üzerinde denenmiş ve uzamsal çözünürlüğü artırılmış hiperspektral görüntüler elde edilmiştir. Elde edilen sonuçlar, literatürdeki en yakın çalışma olan Toplam Değişinti kısıtlı Negatifolmayan Matris-Vektör Tensor Faktörüzasyonu yöntemi ile karşılaştırılmış ve önerilen yöntemin daha iyi performans sergilediği gözlemlenmiştir.

Keywords

Hiperspektral görüntü, Kızılötesi, Veri kaynaştırma, Spektral karışım giderimi, Toplam değişinti

Hyperspectral unmixing with LiDAR-Based total variation regularized non-negative tensor factorization

Abstract

Spectral unmixing is one of the main research areas of hyperspectral image analysis. In recent years, Non-Negative Tensor Factorization based approaches have gained great importance in remote sensing as they do not lose information and can better represent hyperspectral images. The Total Variation approach preserves the edge information while providing piece-wise smoothness. On the other hand, the Light Detection and Ranging sensor provides Digital Surface Model information that gives height information about the observed scene. In this study, hyperspectral unmixing based on tensor factorization is performed to increase the spatial resolution of hyperspectral images by combining LiDAR Digital Surface Model information with Total Variation constrained. Experimental studies are carried out on simulation and real data sets and high spatial resolution hyperspectral images is obtained. The obtained results is compared with the state of the art Total Variation constrained Matrix-Vector Non-Negative Tensor Factorization approach and it is observed that the proposed method obtain better performance.

Keywords

Hyperspectral image, Light detection and ranging, Data fusion, Spectral unmixing, Total variation

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