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Quick and efficient classification of images is important in many Remote Sensing Image (RSI) understanding tasks. The enormous availability of the RSI makes the classification a challenging task, which further needs to design more efficient models. Realizing the wide range of applications of the research area, a new fast Remote Sensing Image Classification (RSIC) framework using an Adaptive Singular Value Decomposition incorporated Extreme Learning Machine (ASVDIncELM) is proposed in this work. The classification framework uses Multi-Scale, Multi-Band (MSMB) Gabor filters to extract Opponent Colour Texture (OCT) features to represent the knowledge patterns. As the input knowledge patterns are High Dimensional (HD), an Adaptive Growing-Pruning Hidden Node Selection (AGPHNS) criterion is integrated with ASVDIncELM. A user-defined heuristic is used to select the optimal number of hidden nodes. The ASVDIncELM framework supports online learning of samples that come as blocks of fixed length. The efficiency of the framework is evaluated using three benchmark RSI datasets- UC Merced (UCM), RS-19, and PatternNet. The results obtained are promising for both offline and online scenarios in terms of Overall Accuracy (OA), Weighted Average (WA) of Precision, Recall, F1-Score, and training time.

A fast adaptive SVDIncExtreme learning machine framework for remote sensing image classification