Land cover mapping using remote sensing optical images with a very high spatial resolution (VHSR) plays an important role in observing the Earth’s surface. However, classification maps are usually affected by salt-and-pepper noise because VHSR optical images usually have a low resolution of ground targets in terms of spectral reflectance. An adaptive region-based post-classification framework (ARPF) is proposed for improving the initial classification map while using a VHSR optical image to further smooth the noise of initial classified maps. First, different from several traditional methods using a single classifier, our proposed ARPF needs more than four different initial classification maps acquired from different classifiers or image features. Second, an adaptive region around each pixel of the gray image is generated with two predefined parameters, and each adaptive region is applied to refine the corresponding pixel of each initial classified map. Finally, all the refined classified maps are merged to obtain the final classification map by coupling adaptive region and majority voting rules. In our experiments, three optical images with VHSR are used to evaluate the proposed ARPF. Compared with three typical relevant post-classification methods, the proposed ARPF can provide a classification map with less noise in visual performance and achieve higher quantitative accuracy while having an advantage in the constant detail of ground targets.
A training sample refining method is proposed to improve the classification performance of very high-spatial resolution (VHR) remote sensing images. The proposed approach involves three major steps. First, for a given image, an initial sample set with a limited number for each class is prepared manually. Second, neighboring pixels around each available labeled pixel are gradually distinguished by an adaptive extension algorithm. When an iterative extension around the available pixel is terminated, the neighboring pixels that are within the extended region are taken into account as candidate training samples. The candidate training sample is then used to refine the signature of each initial sample. Third, when the whole available labeled pixels are scanned and processed pixel-by-pixel in the above manner, the revised training sample set is trained specially for a supervised classifier for classification. Three VHR remote sensing images with limited initial samples are used for evaluating different classifiers and advanced methods based on spatial–spectral features to investigate the feasibility and performance of the proposed approach. Higher classification performance and accuracies are obtained by our proposed approach with respect to the classification maps based on the initial training sample set and an existing method that improves the initial training set by a regular window.
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.