A Deep Neural Network Combined With Context Features for Remote Sensing Scene Classification

Abstract

Scene classification is an important research topic in the field of remote sensing (RS), and deep features from convolutional neural networks (CNNs) have shown good classification performance. However, a key issue is how to effectively combine context features for further improving classification accuracy. In this letter, an end-to-end framework termed deep neural network combined with context features (CFDNN) is proposed for scene classification. At first, the pretrained VGG-16 is transferred as feature extractor to obtain convolutional features. Then, two parallel modules, global average pooling (GAP) and long short-term memory (LSTM), are employed to extract global features and context features, respectively. Finally, a weighted concatenation method is introduced to combine the global and context features. As a result, the CFDNN method can adapt high spatial resolution (HSR) images with arbitrary size and obtain satisfactory classification accuracy. The experimental results on the aerial image data set (AID) demonstrate that the proposed CFDNN method has competitive classification performance compared with some state-of-the-art methods.