Uydu, uzaktan algılama, uydu görüntülerinin yüksek performansta i¸slenmesi ve farklı uydu sensörlerinden elde edilen uydu görüntülerinden nesne çıkarım çalı¸smaları son yıllarda popüler bir konu olmu¸stur. Ülkemizde de ilk yerli alçak yörünge (700 kilometre civarında) uyduları Göktürk2 ve RASAT’ın kurulmasının ardından Ay’a derin gözlem uydusu göndermek de 2023 hedefleri arasında yer almaktadır. ˙Ilgili geli¸smelerle beraber uydu görüntülerinin çe¸sitli amaçlar do˘grultusunda daha etkin bir biçimde kullanılması da kaçınılmazdır. Bu tez kapsamında, uyduların çekmi¸s oldu˘gu parça (mozaik) görüntülerin vektörel forma dönü¸stürülerek E¸sle/˙Indirge paradigmasına dayanan bir mimari ile ölçeklenebilir bir ¸sekilde örülmesi ve ilgili nesnenin çıkarılması sa˘glandı.
Uydu mozaik görüntülerinin zaman-mekânsal veriler olmasından dolayı bilinen piksel tabanlı özellik çıkarımı ve anahtar noktalar (key points) tespitine dayalı metotlarla görüntülerin birle¸stirilmesi çok zor olmakta ve ba¸sarı oranı ise çok dü¸smektedir. Yaptı˘gımız testlerde SIFT, SURF gibi yakla¸sımlarla dü¸sük boyutlu az sayıda görüntü ba¸sarılı bir ¸sekilde birle¸stirilebilmekte uydu görüntülerinde ise tamamen çalı¸smamaktadırlar. Mozaik birle¸stirme i¸slemi hesap yo˘gun bir i¸s olmasının yanında veri yo˘gun da bir i¸stir. Bu problemle ba¸s edebilmek için raster görüntüler E¸sle/˙Indirge ile vektörel forma dönü¸stürülüp vektör görüntülerin birle¸stirilmesi için nokta seti ¸sablonu e¸sle¸stirmesi tabanlı bir yakla¸sım geli¸stirilmi¸stir. Bu yöntem hem E¸sle/˙Indirge mantı˘gına hem de da˘gıtık hesaplama mantı˘gına yatkındır. Bu yakla¸sımda birle¸sime katılacak tüm girdi görüntülerin iki¸serli olarak çakı¸sma olasılıkları belirli bir çakı¸sma oranına göre olu¸sturulmakta ve bir çakı¸sma matrisi elde edilmektedir. Çakı¸sma matrisinden yaralanarak çakı¸san iki görüntüden biri sabit di˘geri dinamik olmak ¸sartıyla dinamik görüntünün sabit olan görüntü üzerine yerle¸stirilmesi için gerekli öteleme miktarları bir öteleme matrisinde tutulmu¸stur. Bu iki matristen (çakı¸sma ve öteleme) yararlanarak bir çakı¸sma çizgesi olu¸sturulmu¸stur. Bu çizgenin dü˘gümleri birle¸smeye girecek görüntüleri temsil edip dü˘güm de˘gerleri ise nihai görüntüde her bir resmin nereye konumlandırılaca˘gını göstermektedir. Önerilen yakla¸sımın de˘gi¸sken sayıdaki farklı girdi boyutlarında görüntüler üzerinde tek bir bilgisayar ve üç bilgisayardan olu¸san bir küme üzerinde testleri yapılmı¸stır. Görüntü boyut ve sayılarının artması ile birle¸stirme süreleri artmı¸s olup özellikle 2000x2000 ve üzeri boyutlu görütülerin küme
üzerinde birle¸stirilmesi daha kısa zaman almı¸stır. Büyük boyutlu çok daha fazla görüntünün daha çok bilgisayar üzerinde birle¸stirilebilece˘gi a¸sikardır.
Özetle geli¸stirilen sistem a¸sa˘gıdaki hedefleri yerine getirmektedir:
• Büyük boyutta ve çok sayıdaki mozaik parça uydu görüntülerin büyük veri mimari çatısı ile ölçeklenebilir ¸sekilde örülebilmesi (Bölüm 5.2’ye bakınız),
• Büyük boyutta ve çok sayıdaki uydu görüntülerinin doküman tabanlı NoSQL veri tabanlarında Hbase’de depolanması (Bölüm 4.1.2’ye bakınız),
• Referans haritalar üzerinden interaktif olarak kullanıcı destekli, yarı otomatik sezgisel nesne tanımlaması (Bölüm 4.1.3’e bakınız),
• Alan sorgularında (range-queries) kapsama problemi çözüm algoritmalarının, nesnenin bütün görüntüsünün elde edilmesi için gereken mozaiklerin tespitine uygulanması (Bölüm 4.1.4’e bakınız),
• Tespit edilen parça görüntülerin E¸sle/˙Indirge büyük veri i¸sleme çatısı ile vektör tabanlı olarak örülmesi (Bölüm 5.2’ye bakınız),
• Uydu görüntülerinden çıkarılıp vektörel modellenen nesnelerin mekânsal veritabanlarında üçüncü ¸sahıslar (di˘ger ara¸stırmacılar) tarafından mekânsal ve topolojik analizlerinde kolaylıkla kullanabilecekleri yapıya getirilmesi (Bölüm 5.2’ye bakınız), • Yapılabilirlik ve ölçeklenebilirlik testlerinin gerçek uydu görüntüleri (LandSat-8) ve farklı görüntüler ile gerçeklenmesi (Bölüm 5.3’e bakınız).
Elde edilen kazanımlar neticesinde tez kapsamında yapılan çalı¸smaların devamı niteli˘ginde a¸sa˘gıdaki çalı¸smalar yapılabilir:
• Kullanıcı tanımlı bir vektör nesnesini birçok uydu görüntüsünde varlı˘gı ara¸stırılarak sorgulama yapılması (image retrivial),
• Açık kaynak Hadoop GIS (SpatialHadoop, Hadoop-GIS gibi) araçları ile entegrasyonu sa˘glanarak mekansal veriler üzerinde çe¸sitli sorguların yapılması, • Önerilen E¸sle/˙Indirge tabanlı yakla¸sımın farklı domainlerde (tıbbi görüntüler vs) farklı problemler için yapılabilirli˘ginin incelenmesi,
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