Correction Of Remote Sensing Images Research Articles

PIF method for relative radiometric correction of remote sensing images

PIF method for relative radiometric correction of remote sensing images

Selection of GCP in geometric correction of remote sensing images

Selection of GCP in geometric correction of remote sensing images

A Perceptually Inspired Variational Method for the Uneven Intensity Correction of Remote Sensing Images

Perceptually inspired color correction methods are characterized by human visual system properties. In this paper, we propose a perceptually inspired variational method for uneven intensity correction of remote sensing images. The proposed method shares the same intrinsic scheme as the Retinex theory, but the reflectance in this method is solved directly within the limited dynamic range and is supposed to comply with the gray world assumption. Considering the smoothness of illumination and the complexity of reflectance, the proposed method integrates L2 norm and total variation prior to inflict varying constraints to different components and regions. The minimum of this variational model is calculated using the steepest descent approach. Experimental results are presented to validate the effectiveness of the proposed method.

Research on Calculating Sky-view Factor with High Precision

Sky-view factor is an important parameter to research terrain radiant correction of remote sensing image,calculating solar radiation of mountainous area,etc.The integral formula of calculating sky-view factor was described.The formula taking account of neighborhood terrain shelter is an analytical approach.The discrete methods of implementing the formula are introduced.After calculating the sky-view factor using the analytical approach and conventional simple approach in Taibai mountain region,the result shows that analytical approach is more scientific and accurate than the conventional simple approach.

Striping Noise Detection and Correction of Remote Sensing Images

This paper presents an image destriping system for correcting striping noise of remote-sensing images. The developed system identifies stripe positions based on edge-detection and line-tracing algorithms. Pixels not affected by striping are used as control points to construct cubic spline functions describing spatial gray level distributions of an image. Detected stripes are corrected by replacing the pixels with more reasonable gray values computed from constructed spline functions. Gray values of clean pixels not affected by stripes are not altered to preserve data genuineness. Several experimental results demonstrate that the developed system can correctly detect stripes in remote-sensing images and effectively repair them. Evaluations of the results based on an quantitative image quality index indicate that the image quality has been improved significantly after destriping. The destriped images are not only visually more plausible but also can provide better interpretability and are more suitable for computerized analysis.