Predicting Missing Image in Remote Sensing Time Series Using Spatial-Temporal-Spectral Data

Abstract

Remote sensing is the acquisition of physical characteristics (reflecting radiation) of the remote object. It can be collected via special cameras or sensors in the satellite or aircraft or weather balloons. Each remote sensing images has multiple spectral bands. The remote sensing images analysis is used by multiple applications like metrological prediction, Land Cover and Land Usage prediction (LCLU), vegetation change detection. Missing image in the remote sensing time series produces a lot of glitches, causing serious upshot in the multi-temporal analysis, when the images at various time stamps are missing over a period of time. The existing work reconstructs missing image in remote sensing time series via spatial and temporal data. The proposed method Tensor-Deep Stacking Network Spatial-Temporal-Spectral (TDSN-STS) helps to reconstructs the missing image in remote sensing time series using spatial, temporal and spectral data. Thus the accuracy of the reconstructed image in TDSN-STS was increased substantially compared to the existing work.