Abstract
Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.
Highlights
The Landsat 7 scan line corrector (SLC) failed to correct the undersampling of the primary scan mirror on May 31, 2003
A novel image restoration approach for filling gaps based on SLIC segmentation and matrix completion reinforced by edges restoration is proposed
The problem of gaps in Landsat 7 SLC-off ETM+ images was addressed by the novel image restoration method proposed
Summary
The Landsat 7 scan line corrector (SLC) failed to correct the undersampling of the primary scan mirror on May 31, 2003. Without the operating SLC, in addition, the areas that have missing pixels are not identical across all multispectral bands. It appears that gaps change positions slightly with spectral bands and produce invalid data in some bands whereas no data in other bands. Al., 2011) for getting SLIC superpixels and Regionalization with Dynamically constrained agglomerative clustering and partitioning (REDCAP) (Guo, 2007) to get image segments .The second step consist of finding the missing area calculated using Edge Matching Algorithm (Salma et al, 2017).The third step is used to fill the gaps in the damaged region performed using Accelerated Proximal Gradient Line Algorithm (APGL) (Salma et al, 2017).
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