Remote Sensing Image Classification Using Deep-Shallow Learning

Recently, classification using multiple classifier system (MCS) has been reported as an effective method to improve remote sensing (RS) image classification. Such systems provide a complementary mechanism to use multiple classifiers, which have shallow architecture to solve the same classification problem; however, the system exhibits shortcomings due to complex ensemble strategy. Deep learning (DL) has been proven to be an advanced method for complex data classification; however, how to use its advantages to overcome the shortcomings of MCS in ensemble strategy for classification accuracy improvement is worthy of study. Thus, with the multiple classifier mechanism and DL architecture, we propose a novel RS image classification framework, namely, deep-shallow learning (DSL), to improve classification accuracy. The DSL framework consists of a shallow learning (SL) layer and a DL layer. The SL layer contains various classifiers with shallow architecture, which can output different classification results for a certain input, whereas the DL layer is formed by DL networks, which can continue learning from the outputs of the SL layer. DSL simulates a human thinking model that continuously learns from the existing learnings to improve learning efficiency. In our experiment, three shallow classification algorithms, i.e., C4.5, k-nearest neighbor, and naive Bayesian, are used to train base classifiers in the SL layer, whereas a deep belief network (DBN) is used to train the DL layer. The experiment results on three different datasets indicate that DSL outperforms other methods in terms of classification accuracy by using backpropagation neural network, bagging, AdaBoost, random forest, multilayer perceptron, and DBN.