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The advancement of Earth observation technologies has enabled comprehensive Earth monitoring at unprecedented levels. Single remote sensing images often fall short in practical applications due to various limitations. In response, the field of remote sensing has evolved to encompass multiple temporal phases, wavelengths and sensors, resulting in a wealth of multi-source remote sensing data. However, most studies have primarily employed single remote sensing images as their data source, with limited research on deep learning for land cover classification using multi-source mixed samples. There is limited research on the influence of network band selection strategies for different land cover categories. Research into network band selection strategies further enhances the accuracy of land cover extraction from multi-source data. Consequently, this paper presents a methodology for deep learning with multi-source mixed samples, investigating strategies for selecting optimal spectral bands for different land cover categories and ultimately aiming to extract various typical land cover classes from multi-source remote sensing data within the same geographic region. We begin by creating a multi-source remote sensing mixed sample dataset and employ channel enhancement methods to enable the model to predict data with different spectral band combinations. Subsequently, we investigate the network's selection of optimal spectral bands for different land cover categories to enhance land cover extraction accuracy. The results indicate that our approach not only improves accuracy but also enhances the model's generalization capabilities.
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