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High-resolution unmanned aerial vehicle (UAV) remote sensing images have super-high ground resolution. Although they provide complete and detailed surface observation data for various engineering applications, the extraction of information from complex and diverse surface scenes is challenging. Characterising surface targets with bright colours and different shapes using samples with fixed sizes and neural networks with fixed network structures at a single resolution is difficult. Therefore, a multi-resolution classification network called structure defined by sample characteristics (SDSC) network was designed in this study. After the SDSC network learned the samples using a multi-resolution strategy and the principle of maximum classification probability, the multi-resolution classification results were integrated into the final classification results to improve their credibility and accuracy. The new method has a better cognitive performance and noise resistance, as well as broad application potential, such that it is more suitable for high-spatial resolution UAV remote sensing images.

Multi-resolution classification network for high-resolution UAV remote sensing images