Mining Deep Semantic Representations for Scene Classification of High-Resolution Remote Sensing Imagery

摘要

Scene classification is one of the most fundamental task in interpretation of high-resolution remote sensing (HRRS) images. Many recent works show that the probabilistic topic models which are capable of mining latent semantics of images can be effectively applied to HRRS scene classification. However, the existing approaches based on topic models simply utilize low-level hand-crafted features to form semantic features, which severely limit the representative capability of the semantic features derived from topic models. To alleviate this problem, this paper propose to build powerful semantic features using the probabilistic latent semantic analysis (pLSA) model, by employing the pre-trained deep convolutional neural networks (CNNs) as feature extractors rather than relying on the hand-crafted features. Specifically, we develop two methods to generate semantic features, called multi-scale deep semantic representation (MSDS) and multi-level deep semantic representation (MLDS), by extracting CNN features from different layers: (1) in MSDS, the final semantic features are learned by the pLSA with multi-scale features extracted from the convolutional layer of a pre-trained CNN; (2) in MLDS, we extract CNN features for densely sampled image patches at different size level from the fully-connected layer of a pre-trained CNN, and concatenate the sematic features learned by the pLSA at each level. We comprehensively evaluate the two methods on two public HRRS scene datasets, and achieve significant performance improvement over the state-of-the-art. The outstanding results demonstrate that the pLSA model is capable of discovering considerably discriminative semantic features from the deep CNN features.