Very High Resolution Image Scene Classification with Semantic Fisher Vectors
Very high resolution (VHR) image scene classification is the most challenging of remote sensing data analysis, that has attracted researchers' attention. To improve the precision of VHR image scene classification, we propose a new method based on convolutional features extracted by convolutional neural network (CNN) First, Visual Geometry Group Network (VGG Net) model is introduced as a feature extractor form the original VHR images. Second, we select the fifth convolutional layer constructed by VGG Net, which is supposed as convolutional features descriptors. Third, based on Improved Fisher Vector (IFV) coding method, we compute the visual word corresponding to the convolutional features of the image scene. We conduct experiments on the public AID benchmark dataset, which contains 30 different areal categories with sub meter resolution. Experimental results demonstrate the effectiveness of the proposed method, as compared with several state of the art methods.
IGARSS 2018 2018 IEEE International Geoscience and Remote Sensing Symposium