Remote sensing image scene classification using deep combinative feature learning

Scene classification shows pivotal role in remote sensing image researches. Since challenges of large similarity between classes, high diversity in each class and huge variations in background, spatial resolution, translation, etc., remote sensing image scene classification still urgently need development. In this paper, we propose a novel method named deep combinative feature learning (DCFL) to extract low-level texture and high-level semantic information from different network layers. First, feature encoder VGGNet-16 is fine-tuned for subsequent multi-scale feature extraction. And two shallow convolutional (Conv) layers are selected for convolutional feature summing maps (CFSM), from which we extract uniform LBP with rotation invariance to excavate detailed texture. Deep semantic features from fully-connected (FC) layer concatenated with shallow detailed features constitute deep combinative features, which are thrown into support vector machine (SVM) classifier for final classification. Extensive experiments are carried out and results prove the comparable advantages and effectiveness of the proposed DCFL contrasting with different state-of-art methods.