In this article, a novel feature selection-based multi-scale superpixel-based guided filter (FS-MSGF) method for classification of very-high-resolution (VHR) remotely sensed imagery is proposed. Improved from the original guided filter (GF) algorithm used in the classification, the guidance image in the proposed approach is constructed based on the superpixel-level segmentation. By taking into account the object boundaries and the inner-homogeneity, the superpixel-level guidance image leads to the geometrical information of land-cover objects in VHR images being better depicted. High-dimensional multi-scale guided filter (MSGF) features are then generated, where the multi-scale information of those land-cover classes is better modelled. In addition, for improving the computational efficiency without the loss of accuracy, a subset of those MSGF features is then automatically selected by using an unsupervised feature selection method, which contains the most distinctive information in all constructed MSGF features. Quantitative and qualitative classification results obtained on two QuickBird remotely sensed imagery datasets covering the Zurich urban scene are provided and analyzed, which demonstrate that the proposed methods outperform the state-of-the-art reference techniques in terms of higher classification accuracies and higher computational efficiency.
An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery
