Fusion of SAR and optical images using pixel-based CNN

Title:

Fusion of SAR and optical images using pixel-based CNN

Creator:

Bandi, Sudheer Reddy, Anbarasan, M., and Sheela, D.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:488a8af5-7d95-4c50-8669-25d4f3d733d3
uuid:488a8af5-7d95-4c50-8669-25d4f3d733d3
doi:10.14311/NNW.2022.32.012

Subject:

deep learning, image fusion, optical data, synthetic aperture radar, and quality metrics

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Sensors of different wavelengths in remote sensing field capture data. Each and every sensor has its own capabilities and limitations. Synthetic aperture radar (SAR) collects data that has a high spatial and radiometric resolution. The optical remote sensors capture images with good spectral information. Fused images from these sensors will have high information when implemented with a better algorithm resulting in the proper collection of data to predict weather forecasting, soil exploration, and crop classification. This work encompasses a fusion of optical and radar data of Sentinel series satellites using a deep learning-based convolutional neural network (CNN). The three-fold work of the image fusion approach is performed in CNN as layered architecture covering the image transform in the convolutional layer, followed by the activity level measurement in the max pooling layer. Finally, the decision-making is performed in the fully connected layer. The objective of the work is to show that the proposed deep learning-based CNN fusion approach overcomes some of the difficulties in the traditional image fusion approaches. To show the performance of the CNN-based image fusion, a good number of image quality assessment metrics are analyzed. The consequences demonstrate that the integration of spatial and spectral information is numerically evident in the output image and has high robustness. Finally, the objective assessment results outperform the state-of-the-art fusion methodologies.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

197-213

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2022 Volume:32 | Number:4

Harvested from:

CDK

Metadata only:

false