Multi-feature Fusion for High Resolution Aerial Scene Image Classification

Abstract

Remote sensing scene classification plays an important role in many applications. Obtaining a high discriminative feature representation is the key of scene classification. The information hidden in different layers of convolutional neural network (CNN) has great potential for enhancing the feature discrimination ability. In this paper, the features from convolutional layers and a set of local features are combined for scene classification. Specifically, deep hierarchical features from different convolutional layers are extracted by a pretrained CNN model, which is used as a feature extractor. A patch-based MS-CLBP method is adopted to acquire local representations. Then the holistic hierarchical and local visual representation is obtained after fisher vector (FV) encoding. Finally, an improved extreme learning machine (ELM) is adopted to classify the scene images based on the obtained FVs. Experimental results show that the proposed methods achieves excellent performance compared with the state-of-the-art classification methods.