iDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması

Hiperspektral (HS) ve kızılötesi (Light Detection and Ranging-LiDAR) algılayıcıları en yeni uzaktan algılama teknolojilerindendir. Son yıllarda, hiperspektral karışım giderimi analizi uzaktan algılama uygulamalarında büyük bir önem kazanmıştır. Spektral değişkenlik hiperspektral görüntülerde bazı nedenlerden dolayı meydana gelebilmektedir. Bu spektral değişkenlik hiperspektral görüntü analizinde ciddi bolluk değeri tahminleme hatalarına sebep olabilmektedir. LiDAR algılayıcısı spektral değişkenlikten etkilenmeyen Dijital Yüzey Modeli (DSM) bilgisini sunmaktadır. Bu çalışmada, hiperspektral görüntülerde spektral değişkenliği azaltmak için Kararlı Bölge Karışım Giderimi (Stable Zone Unmixing–SZU) yaklaşımı LiDAR-DSM verisinin kümeleme bilgisi kullanılarak uygulanmıştır. Deneysel çalışmalar simulasyon ve gerçek veri setleri üzerinde gerçekleştirilmiş ve spektral değişkenliğin her iki veri setinde de azaltıldığı görülmüştür.

LiDAR-aided Spectral Variability Decreasing in Hyperspectral Imagery Based on an Automated Waveband Selection Approach

Hyperspectral (HS) and Light Detection and Ranging (LiDAR) sensors are the two of the newest remote sensing technologies. In recent decades, hyperspectral unmixing analysis has achieved a great importance in remote sensing applications. Spectral variability can occur in hyperspectral images due to some reasons. This spectral variability can cause serious abundance estimation errors in hyperspectral image analysis. On the other hand, LiDAR data provides the Digital Surface Model (DSM) data that does not affected by spectral variability. In this study, in order to decrease the spectral variability on hyperspectral imagery, Stable Zone Unmixing (SZU) approach is used by segmenting of LiDAR-DSM information. Experimental results are carried out on simulation and real data sets and spectral variability is reduced in both images.

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