Estimation of Sensor Noise Parameters from Remote Sensing Data Using Convolutional Neural Networks and Mixed Synthetic and Real Data
Résumé
Characterization of remote sensing (RS) sensors noise is important for optimization and analysis of potential accuracy of subsequent image processing stages (such as filtering or compression, image registration accuracy or objects classification). Blind noise parameters estimation (BNPE) allows analyzing sensor noise directly from noisy images. BNPE is complementary to direct sensor calibration methods and is able to provide additional noise parameters measurements when calibration procedure is not accurate or cannot be performed. Recent BNPE methods show improved performance by utilizing learning-based approach. This paper aims at solving the problem of lack of training data with precise ground truth noise parameters needed for training BNPE models. We focus on learning-based approach using calibrated real data, mixture of synthetic and real data and novel semi-supervised training with uncalibrated real data. The results show that BNPE method training in semi-supervised manner performs better than model-based approach and comparable to supervised training. These results open up possibility of easy extension of training datasets for BNPE methods.