LAUSR

Pixel-based contextual classification methods, including morphological profiles (MPs), extended MPs, attribute profiles (APs), and MPs with partial reconstruction (MPPR), have shown the benefits of using geometrical features extracted from very-high resolution (VHR) images. However, the structural element sequence or the attribute filters that are necessarily adopted in the above solutions always result in computationally inefficient and redundant high-dimensional features. To solve the second problem, we introduce maximally stable extremal regions (MSER) guided MPs (MSER_MPs) and MSER_MPs(M), which contains mean pixel values within regions, to foster effective and efficient spatial feature extraction. In addition, the extremely randomized decision tree (ERDT) and its ensemble version, ExtraTrees, are introduced and investigated. An extremely randomized rotation forest (ERRF) is proposed by simply replacing the conventional C4.5 decision tree in a rotation forest (RoF) with an ERDT. Finally, the proposed spatial feature extractors, ERDT, ExtraTrees, and ERRF are evaluated for their ability to classify three VHR multispectral images acquired over urban areas, and compared against C4.5, Bagging(C4.5), random forest, support vector machine, and RoF in terms of classification accuracy and computational efficiency. The experimental results confirm the superior performance of MSER_MPs(M) and MSER_MPs compared to MPPR and MPs, respectively, and ExtraTrees is better for spectral-spatial classification of VHR multispectral images using the original spectra stacked with MSER_MPs(M) features.