Saliency-Guided Unsupervised Feature Learning for Scene Classification

Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the…

• NN
  National Natural Science Foundation of China

  Awards: 41431175, 61471274
• NK
  National Key Research and Development Program of China

  Awards: 2012CB719905, 2011CB707105