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 convolutiona...

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Bibliographic Details
Main Authors: Chaib, Souleyman, Gu, Yanfeng, Yao, Hongxun, Belkadi, Khaled
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
Convolutional codes
Feature extraction
feature selection
Image analysis
Remote sensing
saliency detection
Scene classification
sparse principal component analysis
Testing
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2018.8518670