Représentations parcimonieuses et apprentissage de dictionnaires pourla compression et la classification d'images satellites
Başlık çevirisi mevcut değil.
- Tez No: 584035
- Danışmanlar: Dr. CHRISTINE GUILLEMOT
- Tez Türü: Doktora
- Konular: Bilim ve Teknoloji, İletişim Bilimleri, Science and Technology, Communication Sciences
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2015
- Dil: Fransızca
- Üniversite: Université de Rennes I
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 236
Özet
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Özet (Çeviri)
This thesis explores sparse representation and dictionary learning methods to compress and classify satellite images. Sparse representations consist in approximating a signal by a linear combination of a few columns, known as atoms, from a dictionary, and thus representing it by only a few non-zero coecients contained in a sparse vector. In order to improve the quality of the representations and to increase their sparsity, it is interesting to learn the dictionary. The rst part of the thesis presents a state of the art about sparse representations and dictionary learning methods. Several applications of these methods are explored. Some image compression standards are also presented. The second part deals with the learning of dictionaries structured in several levels, from a tree structure to an adaptive structure, and their application to the compression of satellite images, by integrating them in an adapted coding scheme. Finally, the third part is about the use of learned structured dictionaries for the classi cation of satellite images. A method to estimate the Modulation Transfer Function (MTF) of the instrument used to capture an image is studied. A supervised classi cation algorithm, using structured dictionaries made discriminant between classes during the learning, is then presented in the scope of scene recognition in a picture.
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