The quality of remote sensing images is directly influenced by the quantization bits of the CCD camera in the satellite. The images with higher quantization bits can contain more feature details of ground objects, therefore, are considered to be of higher quality. But high quantization bits also mean large amounts of data, and the images acquired by the CCD camera need to be compressed to shorten the satellite-to-ground transmission time. The existed compressed image quality evaluation model can hardly assess the loss degree of images with high quantization bits effectively. This article briefly described the main process of evaluation of compressed remote sensing images. And a new evaluation method with both subjective and objective factors was set up for the compressed images with high quantization bits and tested with actual satellite remote sensing images. The results show that the proposed evaluation method is consistent with the human eye subjective feeling and can provide quantitative reference for the comprehensive evaluation of the remote sensing satellite.
The color deviation, which refers to the different between the obtained image and the image under the standard light, is from the light source and reflection characteristic of the object. For satellite remote sensing, the length of the radiation transmission path is different for imaging of different latitudes, especially considering the effects of the atmosphere. Therefore, imaging for high latitude in the winter maybe brings some color deviation into the data. There have some color deviation detection methods for digital camera photo, but they are not fit for the remote sensing data, because of the large image range. In the work, a color deviation detection method for the satellite remote sensing image is developed. And the method is validated using the Landsat 8 images obtained in the winter and summer respectively.
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