KEYWORDS: Image quality, Image compression, Image processing, Remote sensing, Principal component analysis, Algorithm development, Signal to noise ratio
Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling,which can improve efficiency. A method of two-dimensional principal component analysis(2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.
Recently launched spaceborne remote sensors with short-wave infrared (SWIR) spectrum were introduced. Sketch and benefits of uncooled or thermoelectric (TE) cooling linear InGaAs detector were analyzed. A remote sensing imaging system with visible and SWIR spectrums using CCD and InGaAs detectors in one optical system was modeled and calculated. The application of SWIR spectrum was proposed.
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