SEGMENTATION BASED CLOUD AND CLOUD SHADOW DETECTION IN SATELLITE IMAGERY

One of the main source of noises in remote sensingsatellite imageryis regional clouds and shadows of these cloudscaused by atmospheric conditions. In many studies, these clouds and shadows are masked with multi-temporal imagery taken from the same area to decrease effects of misclassification and deficiency in different image processing techniques,such as change detection and NDVI (Normalized Difference Vegetation Index). This problem is surpassed in many studies by mosaicking with different images obtained from different acquisition dates of the same region. The main step of all studies that cover cloud cloning or cloud detection is the detection of clouds from a satellite image. In this study, clouds and shadow patches are classified by using a spectral feature based rule set created after segmentation process of Landsat 8 image. Not only spectral characteristics but also structural parameters like pattern, area and dimension are used to detect clouds and shadows. Ruleset of classification is developed within a transferable approach to reach a scene independent method. Results are tested with different satellite imageries from different areas to test transferability and compared with other state-ofart methods in the literature.

UYDU GÖRÜNTÜLERİNDE BÖLÜTLEME TABANLI BULUT VE GÖLGE BELİRLEME

Uzaktan algılamauydu görüntülerinde atmosfer etkilerinden kaynaklı olarak ortaya çıkan bölgesel bulutlar ve bu bulutların gölgeleri, yapılan çalışmalarda problem oluşturan temel gürültü kaynaklarından biridir. Değişiklik analizi, Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI:Normalized Difference Vegetation Index) hesaplama gibi önemlidijital işlemlerdebulut ve gölge bölgeleri, genel olarak yanıltıcı sonuçlar veren bölgeler olduğundan çoğu zaman maskelenerek işlemler gerçekleştirilmektedir. Bu problem,birçok çalışmada,aynı bölgeden elde edilmişçok zamanlı uydu görüntüleri ile mozaikleme yapılarak aşılmaktadır. Tüm bulut belirleme, gölge belirleme ve klonlama yöntemlerinin ortak adımı, görüntüdeki bulut ve gölge bölgelerinin belirlenmesi işlemidir. Bu çalışmada Landsat 8 görüntüleri kullanılarak, bulut ve gölge bölgeleri, görüntü bölütlemesinin ardından spektral özellik tabanlı bir kural dizisi oluşturularak belirlenmiştir. Bulut ve gölge belirlemede, görüntülerin spektral özelliklerinin yanı sıra, doku ve alan gibi yapısal özellikleri de kullanılmıştır. Oluşturulan kural dizisi,birçok bölgede çalışması amaçlanarak transfer edilebilir bir yapıda oluşturulmuş, test görüntülerinde transfer edilebilirliği test edilmiş ve literatürdeki diğer yöntemlere olan üstünlükleri ve eksiklikleri gösterilmiştir.

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