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With the ongoing development of Earth observation techniques, huge amounts of remote sensing images with a high spectral-spatial-temporal resolution are now available, and have been successfully applied in a variety of fields. In the process, they bring about great challenges, such as high-dimensional datasets (the high spatial resolution and hyperspectral features), complex data structures (nonlinear and overlapping distributions), and the nonlinear optimization problem (high computational complexity). Computational intelligence techniques, which are inspired by biological systems, can provide possible solutions to the above-mentioned problems. In this paper, we provide an overview of the application of computational intelligence technologies in optical remote sensing image processing, including: 1) feature representation and selection; 2) classification and clustering; and 3) change detection. Subsequently …