Object Recognition in Remote Sensing Images Using Combined Deep Features

Abstract

Object recognition, which is also referred as object classification or object type recognition, aims at discriminating object types in remote sensing images. With the availability of high resolution remote sensing images, object recognition attracts more and more attention. Different from traditional methods mainly using hand-crafted features, we propose an object recognition method that combines deep features extracted from a convolutional neural network (CNN) to recognize aircrafts and ships in remote sensing images. The proposed method consists of two stages. In the training stage, images of objects with different types and corresponding labels are exploited to fine-tune a pre-trained CNN. Convolutional features are extracted from a convolutional layer of the fine-tuned CNN and pooled by Fisher Vector, and fully-connected features are extracted from a fully-connected layer of the CNN. These features are combined by concatenation and used to train a support vector machine (SVM). In the test stage, the type of each object is determined by the trained SVM using its combined features. Experiments on two data sets collected from Google Earth demonstrate the effectiveness of our method.