Reconstruction Of Missing Information In Satellite Imagery Using STS-CNN

Reconstruction of missing information in any satellite imagery is very important. Without correct information it is very difficult to identify the area in remote sensing images. In general, there are three different types of noises in satellite imagery such as dead pixels and thick cloud cover. The Failure in Instruments such as Aqua MODIS BAND-6 instrument and ETM+ SLC (Scan Line Corrector)-off condition will lead to missing of information. The existing model consists of Double weighted low rank model Algorithm which is a time taking and less efficient model. In this paper, reconstruction of information in satellite image got done using STS-CNN (Spatial-Temporal-Spectral Convolution Neural Network), which is very efficient and uses faster network for reconstruction of information in satellite imagery.