Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/222510
Title: Polarimetric SAR Image Factorization
Authors: Feng Xu;Qian Song;Ya-Qiu Jin
Year: 2017
Publisher: IEEE
Abstract: This paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.
URI: http://localhost/handle/Hannan/222510
volume: 55
issue: 9
More Information: 5026,
5041
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7941997.pdf4.63 MBAdobe PDF
Title: Polarimetric SAR Image Factorization
Authors: Feng Xu;Qian Song;Ya-Qiu Jin
Year: 2017
Publisher: IEEE
Abstract: This paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.
URI: http://localhost/handle/Hannan/222510
volume: 55
issue: 9
More Information: 5026,
5041
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7941997.pdf4.63 MBAdobe PDF
Title: Polarimetric SAR Image Factorization
Authors: Feng Xu;Qian Song;Ya-Qiu Jin
Year: 2017
Publisher: IEEE
Abstract: This paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.
URI: http://localhost/handle/Hannan/222510
volume: 55
issue: 9
More Information: 5026,
5041
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7941997.pdf4.63 MBAdobe PDF