Download PDFOpen PDF in browser

Effective Image Restoration of SAR Images

EasyChair Preprint no. 6897

7 pagesDate: October 20, 2021


Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. However, a component called Scan Line Corrector failure has seriously limited the scientific applications of ETM+ data since SLC failed permanently on May 31,2003 resulting in about 22% of the image data missing. An effective image restoration method is proposed to fill the missing information in the satellite images. The method is pre-processed by image resize, image enhancement and  Gaussian filtering. The segmentation of satellite images are performed using Simple Linear Iterative Clustering to find the SLIC Super pixels and a dynamic clustering algorithm namely Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning algorithm to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region   and the matrix completion is used to fill the gaps of the satellite images using the Accelerated Proximal Gradient Line algorithm. The Landsat 8 Real images are compared with Landsat 7 Satellite images using the performance metrics namely Peak Signal to Noise Ratio and Root Mean Square Error to evaluate the quality and the error rate of the satellite images. The Results show the capability to predict the missing value    accurately in terms of quality, time without need of external information.

Keyphrases: Keywords Gap filling, Landsat 7 Enhanced Thematic Mapper Plus, matrix completion, remote sensing, Scan Line Corrector, Segmentation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Vaishnavi Pillalamarri},
  title = {Effective Image Restoration of SAR Images},
  howpublished = {EasyChair Preprint no. 6897},

  year = {EasyChair, 2021}}
Download PDFOpen PDF in browser