A rotation invariance spatial transformation network for remote sensing image retrieval

Pengcheng Ding; Shouhong Wan; Peiquan Jin; Chang Zou

doi:10.1117/12.2572945

12 June 2020 A rotation invariance spatial transformation network for remote sensing image retrieval

Pengcheng Ding, Shouhong Wan, Peiquan Jin, Chang Zou

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191P (2020) https://doi.org/10.1117/12.2572945
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

With the development of Geospatial technology and remote sensing technology, a large number of remote sensing images come into application with assist of computers. Although convolutional networks have great performance in computer vision, features extracted by convolutional network doesn’t have the characteristic of rotation invariance, which means the current neural network methods can’t adapt to rotated objects. Considering the multiangle characteristics of remote sensing images, we proposed a Rotation Invariance Spatial transformation Network (RI-STNET) to extract the rotation invariance object features. RI-ST-NET combines convolutional neural networks and the Spatial Transformer Networks (STN) rotating the object to an angle which more easily to identify and is trained by means of Siamese network sharing the same weights of two network branches . Thus RI-ST-NET can adapt to the object features of different rotation patterns which then improved that effectively promote the accuracy of remote sensing retrieval. A Rotation Invariance Spatial Transformation Network combines the advantages of STN and tuple training which can catch the rotation of the same object when used in image retrieval task. A series of evaluation contrast experiments on chosen dataset demonstrate the performance of the proposed method.

Citation Download Citation

Pengcheng Ding, Shouhong Wan, Peiquan Jin, and Chang Zou "A rotation invariance spatial transformation network for remote sensing image retrieval", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191P (12 June 2020); https://doi.org/10.1117/12.2572945

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available