Reconstruction of Remote Sensing Image with Large Thick Cloud Cover using Adaptive Deep Dilated Residual DenseNet

Description

Due to the existence of cloud shadows and the clouds, it restricted the development of optical remote sensing information. Presently, various cloud shifting mechanisms are concentrated on regenerating the remote sensing information which is corrupted by the thin or small cloud layer or cover. Therefore, an automated removal and detection techniques are needed in the complex environment which helps to prevent the important data of the image in the remote sensing. Owing to this, the resolution of the remote sensing image gets affected which fails to provide the clear representations. Thus, it losses the information and makes the system more complicated. Therefore, an improved deep learning-based reconstruction method is implemented to rebuild the lost data of the remote sensing information corrupted by the broad and large clouds. The proposed reconstruction model is developed by a Discrete Wavelet Transform with Adaptive Deep Dilated Residual DenseNet (DWT-ADDi-RD).