The development of coastline detection has been the subject of several reports. An optimized fuzzy cellular automata (FCA) algorithm for SAR image edge detection, which combines newly defined cellular automata (CAs) and fuzzy rules, is proposed. An extended Moore neighborhood is used for cellular automaton. Twelve custom masks were defined to study the edge angles of the radius two neighborhood of the main pixel. Comparing these edge angles with the radius, one neighborhood of the main pixel is useful when deciding whether or not a pixel is an edge pixel. The model was tested on two sets of images. The first dataset contained optical and simulated SAR images and the second contained an Envisat ASAR image and a ScanSAR image. A 3 × 3 Lee filter (as a preprocessing phase) was applied to each subimage containing coastlines, and the subimages were then processed using an FCA edge detector. The results were compared with those from a Sobel edge detector, Roberts edge detector, wavelet transform edge detector, and classic CA model. The results showed that the proposed method is more appropriate for edge detection of SAR images when compared with classic methods. The proposed method and wavelet transform edge detector showed good continuity, but the proposed method dealt better with speckle noise effects.
A new method, MICO-LDASR, is proposed to improve the classification accuracy of fused radar and optical data. The proposed algorithm combines three algorithms: multiplicative intrinsic component optimization (MICO), linear discriminant analysis (LDA), and sparse regularization (SR). MICO-LDASR first corrects the bias fields of the input images by an energy minimization process and then selects the most discriminative image features using a combination of LDA and SR (LDASR) based on a supervised feature selection and learning. Two pairs of fused radar and optical data were used in this study. Features, such as non-negative matrix factorization and textural features, were extracted from the original and bias corrected images, and, following the formation of two different types of feature matrices, the matrices were optimized based on LDASR and utilized in the two learned and unlearned forms as the inputs to rotation forest and support vector machine classifiers. The results showed that classification accuracy is greatly improved when implementing MICO-LDASR on feature matrices of Sentinel and ALOS-fused data.
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.