Abstract:
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Usually, some different categories share common...Show MoreMetadata
Abstract:
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Usually, some different categories share common patterns, which make these categories look similar. This makes the classification of such categories a challenging task. In this paper, we propose a novel dictionary learning based bilayer classification algorithm to solve this problem. Using SIFT descriptor, instead of directly classifying an image, we separate the classification in two steps. In the first step, the similar categories are clustered to be as a new category for the first classification layer. In this step, the inter-class variation are maximized. The second layer is designed to classify the similar categories clustered in the same group. Experimental results show the superiority of our method compared to the state-of-the-art methods using UCMerced LandUse dataset.
Date of Conference: 10-15 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2153-7003