In order to enhance the local feature’s describing capacity and improve the classification performance of high-resolution (HR) satellite images, we present an HR satellite image scene classification method that make use of spatial information of local feature. First, the spatial pyramid matching model (SPMM) is adopted to encode spatial information of local feature. Then, images are represented by the local feature descriptors and encoding information. Finally, the support vector machine (SVM) classifier is employed to classify image scenes. The experiment results on a real satellite image dataset show that our method can classify the scene classes with an 82.6% accuracy, which indicates that the method can work well on describing HR satellite images and classifying different scenes.
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.