Accuracy improvement and evaluation measures for registration of multisensor remote sensing imagery

Öz

Intensity based image registration methods are widely used in fine geometric registration of multisensor images. Accordingly, for images that are compared through translation of image templates, position where similarity measure is maximized is assumed to indicate best registration. Image registration quality is of crucial importance especially for studies that have high geometric accuracy requirements; e.g. image fusion, change detection, multichannel segmentation, and Digital Terrain Model DTM generation. Accuracy of image registration is conventionally evaluated by means of error measures e.g. RMSE obtained through comparison of coordinates of control points from the target and the reference / ground truth. However, especially for multisensor images with low spatial resolution component, difficulty in precisely positioning control points inhibits both sub pixel accuracy and evaluation of the registration. In this study, three widespread measures in intensity-based image registration namely, Normalized Cross Correlation NCC , Mutual Information MI , and Phase Correlation PC are tested for registering images acquired from EO-1 Hyperion and IKONOS sensors. We propose the use of ‘global similarity’ and ‘inverse consistency’ measures for evaluating the performance of these intensity based automated registration methods.

Anahtar Kelimeler

• Image registration,Intensity-based,Similarity measure,Accuracy assessment,Hyperion,IKONOS

Farklı alıcılardan elde edilen uydu görüntülerinin çakıştırılmasında başarım artırma ve değerlendirme ölçütleri

Öz

Farklı uydu alıcılarından alınan görüntülerin hassas biçimde çakıştırılmasında parlaklık temelli yöntemler yaygın olarak kullanılmaktadır. Buna göre, ötelenerek birbiri ile karşılaştırılan görüntü parçalarında, benzerlik ölçütünü enyüksekleyen konum, en iyi çakışma olarak kabul edilir. Çakıştırmanın kalitesi özellikle imge kaynaştırma, değişim tespiti, çok kanallı bölütleme, Sayısal Arazi Modeli SAM üretimi vb. çalışmalar için kritik öneme sahiptir. Çakıştırma başarımı genellikle hedef imge ve referans / yer doğrusu üzerinde bulunan ortak nesne koordinatlarının karşılaştırılmasından elde edilen hata ölçütleri ile ör. RMSE ile değerlendirilir. Ancak, özellikle farklı alıcılardan elde edilen görüntülerde düşük çözünürlüklü bir bileşen var ise, kontrol noktalarını konumlandırmadaki güçlük, çakıştırmada piksel altı başarımı düşürmekte ve çakıştırma değerlendirmesini zorlaştırmaktadır. Bu çalışmada farklı alıcı karakteristiklerine sahip görüntülerin parlaklık temelli otomatik yöntemlerle çakıştırılmasında yaygın olarak kullanılan üç yöntem; Normalize Çapraz Korelasyon NCC , Ortak Bilgi MI ve Faz Korelasyonu PC , EO-1 Hyperion ve IKONOS alıcılarından elde edilen görüntüleri çakıştırmak üzere test edilmektedir. Her bir yönteme göre elde edilen çakıştırma sonuçlarının başarımını değerlendirmek üzere, ‘global benzerlik’ ve ‘ters tutarlılık’ ölçütlerinin kullanımı önerilmektedir

Anahtar Kelimeler

• İmge çakıştırma,Parlaklık temelli,Benzerlik ölçütü,Başarım değerlendirme,Hyperion,IKONOS

Kaynakça

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