In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparent zero-order fuzzy IF...THEN... rules with a prototype-based nature. The DRB classifier can self-organize "from scratch" and self-evolve its structure. By employing the pretrained deep convolution neural network as the feature descriptor, the proposed DRB ensemble is able to exhibit human-level performance through a transparent and parallelizable training process. Numerical examples using benchmark data set demonstrate the superior accuracy of the proposed approach together with human-interpretable fuzzy rules autonomously generated by the DRB classifier.
|Nifer y tudalennau||5|
|Cyfnodolyn||IEEE Geoscience and Remote Sensing Letters|
|Dyddiad ar-lein cynnar||05 Chwef 2018|
|Dynodwyr Gwrthrych Digidol (DOIs)|
|Statws||Cyhoeddwyd - 01 Maw 2018|
|Cyhoeddwyd yn allanol||Ie|