Saliency-Guided Unsupervised Feature Learning for Scene Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 4; pp. 2175 - 2184
• Main Authors: Zhang, Fan, Du, Bo, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autoencoder; Categories; Classification; Coding; Data mining; Datasets; Encoding; Extractors; Feature extraction; Image detection; Kernel; Learning; Regularization methods; saliency detection; scene classification; Statistics; Support vector machines; Training; unsupervised feature learning; Vectors; Australia, New South Wales, Sydney; saliency detection; scene classification; unsupervised feature learning; Autoencoder
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2357078

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 scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called "dropout," which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.