Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network

TL;DR

This paper introduces a unified deep learning framework that effectively reconstructs missing information in remote sensing images by leveraging spatial, temporal, and spectral data, addressing multiple common issues simultaneously.

Contribution

It proposes a novel unified spatial-temporal-spectral CNN model capable of solving various missing data reconstruction tasks in remote sensing images.

Findings

High effectiveness in reconstructing missing data in simulated experiments.

Successful application to real-world remote sensing data.

Outperforms existing methods in accuracy and versatility.

Abstract

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial-temporal-spectral framework based on a deep convolutional neural network (STS-CNN) employs a unified deep convolutional neural network combined with spatial-temporal-spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line Corrector (SLC)-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.