Abstract
HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.
Highlights
Hyperspectral remote sensing increased spectral dimension on basis of traditional two-dimensional remote sensing
In order to cope with the problem of poor local data recovery, this paper proposes a new matching algorithm of adaptive window, The core idea of the algorithm is that it can adjust intelligently size of matching window according to different local information to match two images
The performance of adaptive window matching method is best, especially in column mean curve, image quality factor IQ, peak signal to noise ratio PSNR and image correlation coefficient H, and it is far superior to wavelet analysis method with histogram matching and wavelet domain recovery algorithm based on PDE variational model
Summary
Hyperspectral remote sensing increased spectral dimension on basis of traditional two-dimensional remote sensing. Stripe noise is one of interference factors of hyperspectral remote sensing data, mainly caused by inconsistent responses of CCD detection units in hyperspectral imager [1]. It seriously affects on the data interpretation and information extraction. The matching precision was increased, but the algorithm failed to resolve complicated ground objects Based on these theories, Wegener[4] put forward an improved histogram matching algorithm, and applied it to data recovery on multisensor remote sensing images. A recovery algorithm via adaptive window matching method is proposed to eliminate the stripe noise in HJ-1A hyperspectral remote sensing data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
