An unsupervised feature learning framework based on auto-encoder is proposed to learn sparse feature representations for remote-sensing imagery retrieval in this letter. The low-level feature descriptors are extracted and exploited to learn a set of feature extractors, which are then used to encode the low-level feature descriptors to generate new sparse features. The learned feature representations are applied to aerial images randomly selected from the University of California Merced data set. The results indicate that the performance of our proposed framework is comparable or superior to that of the state-of-the-art method. The framework is proved to be an effective approach to manage the huge volume of remote-sensing data and to retrieve the desired remote-sensing imagery.
ASJC Scopus subject areas
- Earth and Planetary Sciences (miscellaneous)
- Electrical and Electronic Engineering