An Efficient Reconstruction Framework for Remote Sensing Image with Large Thick Cloud Cover using Adaptive Deep Dilated Residual DenseNet

Gauri Dhopavkar, Achukatla Valli Bhasha (), Reema Mathew A, Rakesh Roshan and Arun Maitrey Konda
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Gauri Dhopavkar: Department of Computer Technology, Yeshwantrao Chavan College of Engineering (YCCE) Nagpur, Wanadongri, Maharashtra 441110, India
Achukatla Valli Bhasha: Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Balaji Nagar, Kadapa 516003, Andhra Pradesh, India
Reema Mathew A: Department of Computer Science and Engineering, Vimal Jyothi Engineering College, Kannur District, Chemperi, Kerala 670632, India
Rakesh Roshan: Department of Data Science, Anurag University, Hyderabad, Telangana 500088, India
Arun Maitrey Konda: Computer Science and Engineering (Data Science), Gokaraju Rangaraju Institute of Engineering and Technology, Kukatpally, Hyderabad, Telangana 500090, India

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 02, 1-35

Abstract: 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, 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). By employing the bicubic interpolation-based down-sampling and up-sampling, the overall High Resolution (HR) images are transformed into Low-Resolution (LR) images. After that, the DWT structure helps to produce LR wavelet Sub-Bands (SBs) for LR images and HR wavelet Sub-Band (SB) for the HR images. By taking the differences between HR and LR wavelength, the residual image is produced. Training and testing phases are performed in this developed model. During the training stage, the remaining image of the entire image is trained by ADDi-RD with LR wavelet SBs as the input and the remaining image as the goal. In the testing module, the LR wavelet SBs query image is fed into ADDi-RD, which acquires the corresponding remaining image. Thus, the produced remaining image with LR wavelet SBs is applied to the Inverse Discrete Wavelet Transform (IDWT) to acquire the resultant super-resolution image. The significant goal of this proposed model is to enrich the efficiency of the ADDi-RD by optimising the parameters using a Random Value Enhanced Single Candidate Optimiser (RVE-SCO). In the end, the implemented system attains enhanced premium outcomes that are contrasted to the conventional approaches.

Keywords: Image reconstruction; remote sensing image; large and thick cloud cover; Discrete Wavelet Transform; Adaptive Deep Dilated Residual Densenet; super resolution image; Random Value Enhanced Single Candidate Optimiser (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S021964922550008X

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