Clear-View: A dataset for missing data in Remote Sensing Images

Abstract

This manuscript presents the first-ever dataset made for supervised learning on reconstructing missing data in remotely sensed data. The types of noises present in this dataset are 1) Salt and pepper noise, caused by an error in transmission, analog-digital converter error, 2) The Landsat ETM + Scan Line Corrector (SLC)-of a problem, caused because of the poor performance of satellite sensors, cross-talk between sensors, etc. 3) Presence of thick clouds in its view due to poor atmospheric conditions. Usually, the remotely sensed data suffer an information loss because of satellite sensors' internal malfunction or poor atmospheric conditions such as thick clouds. Losing any pixel due to any external/internal error leads to a huge information loss in the images due to high spatial resolution and further tasks like detection, classification, segmentation, and many more to be applied to it. Therefore, it becomes an important task to regain the lost data before applying any other algorithm. This dataset contains a total of 21,080 images with a spatial resolution of 0.3m and 1.5m. The dataset is accessible at https://sites.google.com/view/clearviewdataset.