TY - GEN
T1 - Rotation-Invariant Latent Semantic Representation Learning for Object Detection in VHR Optical Remote Sensing Images
AU - Yao, Xiwen
AU - Feng, Xiaoxu
AU - Cheng, Gong
AU - Han, Junwei
AU - Guo, Lei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Object detection in very high resolution (VHR) optical remote sensing images is a fundamental yet challenging problem for the field of remote sensing image analysis. The detection performance is heavily dependent on the representation capability of the extracted features. Recently, convolutional neural networks (CNNs) have made a breakthrough for various applications in nature images. However, it is problematic to directly apply CNN to perform object detection in VHR optical remote sensing images due to the problem of object rotation variations. To address this issue, a novel rotation invariant probabilistic Latent Semantic Analysis (RI-pLSA) model is proposed to learn latent semantic representations for object detection. This is achieved by imposing a rotation-invariant regularization term on the objective function of pLSA to enforce the learned representation from all rotations of the same sample to be as consistent as possible. Additionally, the proposed RI-pLSA model takes the CNN features as input, which generates more powerful semantic representation for object detection. Comprehensive experiments on a publicly available ten-class object detection dataset demonstrate the superiority and effectiveness of our method compared with state-of-the-arts.
AB - Object detection in very high resolution (VHR) optical remote sensing images is a fundamental yet challenging problem for the field of remote sensing image analysis. The detection performance is heavily dependent on the representation capability of the extracted features. Recently, convolutional neural networks (CNNs) have made a breakthrough for various applications in nature images. However, it is problematic to directly apply CNN to perform object detection in VHR optical remote sensing images due to the problem of object rotation variations. To address this issue, a novel rotation invariant probabilistic Latent Semantic Analysis (RI-pLSA) model is proposed to learn latent semantic representations for object detection. This is achieved by imposing a rotation-invariant regularization term on the objective function of pLSA to enforce the learned representation from all rotations of the same sample to be as consistent as possible. Additionally, the proposed RI-pLSA model takes the CNN features as input, which generates more powerful semantic representation for object detection. Comprehensive experiments on a publicly available ten-class object detection dataset demonstrate the superiority and effectiveness of our method compared with state-of-the-arts.
KW - convolutional neural networks (CNNs)
KW - Object detection
KW - remote sensing images
KW - rotation invariant probabilistic Latent Semantic Analysis (pLSA)
UR - http://www.scopus.com/inward/record.url?scp=85077699148&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8899285
DO - 10.1109/IGARSS.2019.8899285
M3 - 会议稿件
AN - SCOPUS:85077699148
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1382
EP - 1385
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -