Sparse representation for remote sensing images of long time sequences

Adaptive sparse representations of signals have drawn considerable interest in the past decade. In this paper, we address the problem of training dictionaries for massive images and propose a new algorithm for adapting dictionaries by extending the classical K-SVD based on only a single image. The a...

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Vydáno v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 1293 - 1296
Hlavní autoři: Jing Wen, Peng Liu, Lajiao Chen, Lizhe Wang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2014
Témata:
Algorithm design and analysis
Dictionaries
PSNR
Remote sensing
Signal processing algorithms
Training
Yttrium
ISSN:2153-6996
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Shrnutí:Adaptive sparse representations of signals have drawn considerable interest in the past decade. In this paper, we address the problem of training dictionaries for massive images and propose a new algorithm for adapting dictionaries by extending the classical K-SVD based on only a single image. The approach presented in this paper aims at training the adapting dictionary from massive samples, other dictionary learning methods such as Online Dictionary Learning (ODL) and Recursive Least Squares Dictionary Learning Algorithm (RLS-DLA) also could train the dictionary by using relative large samples. Our method is competed with the above two state-of-the-art dictionary learning methods. Experiments demonstrate the effectiveness of the proposed dictionary learning in dealing with massive spatial-temporal remote sensing.
ISSN:2153-6996
DOI:10.1109/IGARSS.2014.6946670