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To improve remote sensing image classification precision, we propose a novel method which is based on super pixel and adaptive weighted K-Means. First, super pixel segmentation algorithm is used to divide input images into irregular blocks which remain their semantic information and boundaries. And then, SIFT, GIST, Census, Gabor, and Color histogram, and many other types of features are extracted...
The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. In this paper, we propose a hybrid of KNN and MARS which deals naturally with the multi-class setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local...
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