Hyperspectral remote sensing image classification achieved good effect using support vector machine (SVM) even with very few training samples. But due to restrictions on the number of samples, it is hard to further enhance classification accuracy when only using spectral information. On the other hand, one can improve the classification accuracy by increasing the training samples when the training samples are few. Accordingly, we present a method of extending the training samples by using spatial information. In this method, the classes of samples contained in one segmentation region are treated as the same class and the class labels of all the pixels in this region are decided by the class labels of the training samples contained in it. These new samples are then named as the extended training set. Experiments show that the proposed method in this paper has better effect than the direct use of majority voting method.
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.