A hierarchical learning paradigm for semi-supervised classification of remote sensing images

In this paper, we present a new semi-supervised method for the classification of hyperspectral and VHR remote sensing images. The method is based on a hierarchical learning paradigm which is composed of multiple layers feeding into each other: 1) feature extraction layer, 2) classification layer, and 3) spatial regularization layer. In the feature extraction layer, the method employs morphological operators. In case of hyperspectral images, a dimensionality reduction step is first applied using an algorithm such PCA. In layer 2, the Extreme Learning Machine is trained and used to build an initial classification map of the image. Finally, in layer 3, a regularization step is applied to exploit spatial information between all pixels in the image. The Random Walker (RW) algorithm is used for this purpose, which uses the output results of layer 2, such as the class map and the posterior probabilities, as inputs. Initial results are obtained using the PAVIA dataset, which outperform the state-of-the-art methods in terms of accuracy and execution times.