DocumentCode :
2113520
Title :
Remote Sensing object recognition based on transfer learning
Author :
Zhiping Dan ; Nong Sang ; Yanfei Chen ; Xi Chen
Author_Institution :
Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2013
fDate :
23-25 July 2013
Firstpage :
930
Lastpage :
934
Abstract :
The deviation of an object´s real data distribution from the known training data distribution would lead to low reliability of object recognition. To tackle this problem for Remote Sensing (RS) images, we propose a novel object recognition method based on transfer learning. The feature vectors of an object are first extracted by a joint Local Binary Pattern (LBP). The transfer learning is then employed to find the common parameter set among feature spaces of the object under different distributions. Through extensive experiments, it has been shown that a significant improvement on the accuracy is has been brought by the proposed novel method.
Keywords :
feature extraction; object recognition; remote sensing; vectors; LBP; feature vector extraction; joint local binary pattern; real data distribution; remote sensing images; remote sensing object recognition; training data distribution; transfer learning; Accuracy; Aircraft; Classification algorithms; Feature extraction; Learning systems; Object recognition; Training data; image processing; machine learning; object recognition; remote sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location :
Shenyang
Type :
conf
DOI :
10.1109/FSKD.2013.6816328
Filename :
6816328
Link To Document :
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