We have developed a system that applies deep learning application super-resolution (SR) to multispectral and hyperspectral geospatial satellite imagery to deduce higher resolution images from lower resolution images while maintaining the original color of the lower resolution pixels. A super-resolution model, which uses Deep Convolution Neural Networks (DCNNs), is trained using individual image bands, a large crop size or tile size of 512 × 512 pixels, and a de-noise algorithm. Applying our algorithms to maintain the original color of the image bands improves the quality metrics of the super-resolution images as measured by peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of super-resolution images. One of the most important applications of satellite images is to automatically detect small objects such as vehicles and small boats. With super-resolution images generated by our system, the object detection accuracy (recall and precision) has improved by 20% with Planet® multispectral satellite images.
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