Relative Gain Characterization and Correction of Pushbroom Sensors Based on Lifetime Image Statistics and Wavelet Filtering
Developed a tool that is able to reduce stripes from imagery acquired by pushbroom sensors caused by detector-to-detector non-uniform response. This tool concentrates on two methods for the minimization of stripes. The first method estimates the best set of relative gains by identifying the best type of image based on histogram equalization from the lifetime image statistics. The second method is based on cosmetic de-striping that minimizes stripes using wavelet filtering. Three different algorithms based on wavelets are derived: Low Frequency Sub-band (LFSB), High Frequency Sub-band (HFSB) and All Frequency Sub- band (AFSB). In each of these approaches, the main idea is to decompose the image into different frequency components using a wavelet transform, apply an appropriate filter to various image components to remove stripes, and reconstruct the image using a corresponding inverse wavelet transform.