Uzaktan algılamada görüntü birleştirme algoritmalarının karşılaştırılması
Comparison of image fusion algorithms in remote sensing
Yazar: ALPER AKOĞUZ
Danışman: PROF. DR. SEDEF KENT PINAR
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Telekomünikasyon Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2013
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Görüntü birleştirme = Image merging ; Görüntü işleme = Image processing ; Görüntü işleme algoritmaları = Image processing algorithms ; Görüntü işleme yöntemleri = Image processing methods ; Uydu = Satellite ; Uydu görüntüleri = Satellite images ; Uydu verileri = Satellite data

Özet:
1972'de ilk yeryüzü gözlem uydusu Landsat-1'in hizmet vermeye başlamasından bu yana uydu teknolojileri çok gelişmiş olmasına rağmen optik uzaktan algılamada, fiziksel ve teknolojik limitler sebebiyle, bazı uydu algılayıcıları algılanan bölgenin ve/veya bölgedeki cisimlerin özelliklerinin ön plana çıkmasını (çeşitli spektral bandlarda algılama yaparak) spektral olarak sağlarken, kimi algılayıcılar ise bu ayrışımı mekansal olarak sağlamaktadır.Görüntü birleştirme algoritmaları sayesinde, pankromatik (monokromatik) algılama yapan yüksek mekansal detaya sahip olan görüntüler ile yüksek spektral çözünürlüğe sahip multispektral (elektromanyetik görünür bölge) görüntülerin bilgilerinin birleştirilerek spektral ve mekansal çözünürlüğü yüksek görüntüler elde edilmesi konusunda çeşitli çalışmalar gerçekleştirilmiş olup farklı modeller üzerinden sonuç görüntüleri elde edilmiştir. Elde edilen görüntülerle ilgili esas alınan bu problemin iki boyutlu bir enterpolasyon problemi olarak ele alınmasının gerekliliği ve füzyon dahilinde bağımsız görüntülerde yer alan spektral veya mekansal çözünürlüğün tam anlamıyla sonuç görüntülerine aktarılamaması sorunu yer almaktadır.Yapılan çalışmalarda zaman içerisinde bu görüntülerin başarımı ile ilgili ölçütler de tanımlanmış olup ilgili çalışmada, 1980'lerden günümüze geliştirilmiş bu yapıların uzaktan algılamada görüntü birleştirme üzerine çalışan akademik grupların kabul etmiş olduğu dört ana kategoride incelenmiş olup bu kategoriler: modülasyon bazlı, bileşen değişim, çoklu çözünürlük analizi ve karma yöntemler olarak adlandırılmaktadır.Modülasyon bazlı yöntemde Brovey yöntemi incelenmiş olup, bileşen değişim yönteminde PCA, IHS, Gram-Schmidt yöntemleri ele alınmıştır. Çoklu çözünürlük analizinde frekans bölgesinde süzgeçleme ve Wavelet yöntemleri ifade edilmiş; karma yöntemlerde ise bileşen değişim yöntemleri olan IHS ve PCA ile Wavelet birlikte kullanılarak görüntü birleştirme gerçekleştirilmiştir.Detaylı olarak gösterilen bu yapıların görüntü birleştirme topluluğunun kabul etmiş olduğu kalite değerlendirme yöntemleri üzerinden karşılaştırmalı analizi yapılmış olup İstanbul Teknik Üniversitesi içerisinde kurulmuş olan Türkiye'nin ilk uydu yer istasyonu olan Uydu Haberleşmesi ve Uzaktan Algılama Uygulama Araştırma Merkezi (UHUZAM) bünyesinde algılanan SPOT 4 uydu görüntülerinin birleştirilmesi ile ilgili olarak araştırmalarda ve çalışmalarda kullanılabilecek alternatif bir model amaçlanmaktadır.

Abstract:
The advances in satellite technologies mainly assisted the researchers of Earth-observation on several analysis. Although there can be acquired high resolution images there stands a drawback between spectral and spatial resolution. In the satellite sensor there shall be acquired images either including numerous spectral bands from the electromagnetic wave spectrum which are called Multispectral (MS) or having sharp and detailed single band imagery which is called as Panchromatic (Pan) image.MS imagery have richer spectral resolution and Pan imagery offers better spatial resolution so that image fusion is concerned to increase the pixel resolution and keep spatial resolution high by combining spectral and spatial information from multiple images of the same co-registered region. These results are appropriate for analysing on post-processing on numerous disciplines such as Geographical Information Systems (GIS), Remote Sensing (RS) and various civil and military applicationsOn satellite image fusion, many algorithms were developed since 1970s to gather achievement on this subject and can be classified in four main categories: Modulation-based, Component Substitution (CS), Multi Resolution Analysis (MRA) and Hybrid Methods.Firstly, Modulation-based methods like Brovey is dealing about using some intensity modulation ratio to modulate spatial information from the high resolution Pan scene and adding to low resolution MS scene.Secondly, Component Substitution Method is mainly regarded pixel based method on adding some high frequency information into MS scenes with making use of a transformation such as Principle Component Analysis (PCA), Intensity-Hue-Saturation (IHS), and Gram-Schmidt (GS).Principle Component Analysis tries to transform the multispectral data to a new domain that have perpendicular axis lines and the effect of these domain lines are determined with larger eigenvalues. The idea is to swap the first principle component with histogram matched Pan scene (with using first principle component as reference cumulative distribution) to PC plane and applying an inverse PCA to these data set to obtain fused scene.Intensity-Hue-Saturation method is involving about creating Intensity image and as same as PCA method, changing it with histogram matched Pan scene (with using intensity image as reference cumulative distribution) and applying an inverse IHS to obtain fused image.Gram-Schmidt method is apatented method to obtain fused image by using Gram-Schmidt transformation, mainly regarded as Gram-Schmidt Orthogonalization or QR factorization. Low pass filter applied Pan scene and MS scene bands are used in Gram-Schmidt transform to obtain orthonormal vectors which are perpendicular to each other similar to PCA transformation. Since it is a component substituion method, first orthonormal vector is swapped with the Pan scene which is histogram matched with first vector of Gram-Schmidt transformed matrix.Moreover, in the third fusion category, there is concerned a multi resolution filtering analysis (extraction of data into Low Pass and High Pass filtered versions) on MS and Pan scenes and combining them with an appropriate method such as Smoothing Filter Intensity Modulation (SFIM), High Frequency Modulation (HFM) and Wavelet (Additive & Substitution based) rather than observing a weighted combination of MS and Pan scenes in the first method.In Smoothing filter-based intensity modulation, there is used an intensity moduiaton ratio to modulate spatial detals of Pan image to MS scene. There is thought that spatial details can be extracted by appling low pass filter to multiplication of Pan and MS bands. For design of the low pass filter kernel, it is important to obtain the ratio of the pixel sizes MS and Pan.High frequency modulation is involving about nodulating spatial information justl like the SFIM method, but differently there is not a strict definition to design low pass filter and applying it to MS scene.Discrete wavelet transform is a special method to get the discrete results in scale domain, rather than dealing complex ?may reach infinite numbers of transformed- data in the frequency domain of Fourier transform. Wavelet analysis is made by quadrature mirror filters which are filtering high passband and low passband so that there can be obtained low pass and high pass filtered of the same data. Since the study area is two dimensional, there can be obtained Low-Low filtered in both row and column domain, Low-High (which means of horizontal information), High-Low (vertical information) and High-High (diagonal information). After applying wavelet decomposition in both MS scene and Pan scene which is histogram matched with the intensity image. Low frequency details of MS scene used since spectral information is stored in low frequencies while high frequency details of Pan scene used for spatial information and they are combined to apply inverse wavelet transform to obtain pansharpened scene.In the final category, it is used hybrid techniques on merging MS and Pan images, firstly transforming the multispectral scene by using PCA or IHS and secondy first principle component or intensity image with histogram matched Panchromatic scene are both applied multi resolution analysis with Wavelet decomposition with several quadrature mirror filters. Using low frequency details from principle component or intensity image and high frequency details from histogram matched Pan scene, and combine them with inverse discrete wavelet transformation. Finally, there shall be constructed the fused scene by performing inverse IHS or inverse PCA on the merged data of constructed I or first principle component and the remaining data set.In this study, cloud-free SPOT 4 co-registered panchromatic and multispectral scene pairs have been used and chosen according to specific condition of having ?Better Technical Quality? ( in terms of having better Signal-to-Noise Ratio [SNR]), The region of interest is chosen including Istanbul Technical University Main Campus Area which is located in Maslak, Istanbul. Both Pan and MS scenes were down-sampled by a factor of two and fused scene was compared with the original MS scene, regarding as comparison reference scene.The comparison analysis was done on computation times of serial and parallel implementations, as well as on several fusion quality assessments such as Spectral Angle Mapper (SAM), Erreur Relative Globable Adimensionelle de Synthése (ERGAS) and Relative Average Spectral Error (RASE) on different methods to determine the most efficient fusion method.