Land Covers Classification from LiDAR-DSM Data Based on Local Kernel Matrix Features of Morphological Profiles

Accurate land cover classification from the digital surface model (DSM) obtained from LiDAR sensors is a challenging topic that researchers have considered in recent years. In general, the classification accuracy of land covers leads to low accuracy using a single-band DSM image. Hence, it seems necessary to develop efficient methods to extract relevant spatial information, which improves classification accuracy. In this regard, using spatial features based on morphological profiles (MPs) has significantly increased classification accuracy. Despite MPs' efficiency in increasing the DSM's classification accuracy, the classification accuracy results under the situation of limited training samples are not still at satisfactory levels. The main novelty of this paper is to propose a new feature space based on local kernel descriptors obtained from MP for addressing the mentioned challenge of MP-based DSM classification. These innovative feature vectors consider local nonlinear dependencies and higher-order statistics between the morphological features. The experiments of this study are conducted on two well-known DSM datasets of Houston and Trento. Our results show that support vector machine (SVM)-based DSM classification with the new local kernel features achieved an average accuracy of 93.75%, which is much better than conventional SVM classification with single-band DSM and MP features (by about 57% and 11.5% on average, respectively). Additionally, our proposed method outperformed two other DSM classification methods by an average of 4.7%.