TY - GEN
T1 - Very high resolution image scene classification with semantic fisher vectors
AU - Chaib, Souleyman
AU - Gu, Yanfeng
AU - Yao, Hongxun
AU - Belkadi, Khaled
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - Feature selection
KW - Saliency detection
KW - Scene classification
KW - Sparse principal component analysis
UR - https://www.scopus.com/pages/publications/85064190603
U2 - 10.1109/IGARSS.2018.8518670
DO - 10.1109/IGARSS.2018.8518670
M3 - 会议稿件
AN - SCOPUS:85064190603
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6844
EP - 6847
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -