We present a two-stage method for remote sensing image ship detection. The proposed approach efficiently detects ships in remote sensing images. Firstly, a light-weight classification network is used to classify different regions. In second stage, we design a detection framework to detect ships in sub-images, which are considered to contain object in the first stage. To solve the scale problems in object detection, our detection network is built on feature pyramid network, but we explicitly assign object into corresponding feature maps based on size. In our proposed framework, instead of using anchors, we predict object center point and the offsets to bounding box. The experiment results show that our proposed method has a good performance in terms of speed and accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.