Fusion and classification of multi-temporal SAR and optical imagery using convolutional neural network

ABSTRACT

Remote sensing image classification is difficult, especially for agricultural crops with identical phenological growth periods. In this context, multi-sensor image fusion allows a comprehensive representation of biophysical and structural information. Recently, Convolutional Neural Network (CNN)-based methods are used for several applications due to their spatial-spectral interpretability. Hence, this study explores the potential of fused multi-temporal Sentinel 1 (S1) and Sentinel 2 (S2) images for Land Use/Land Cover classification over an agricultural area in India. For classification, Bayesian optimised 2D CNN-based DL and pixel-based SVM classifiers were used. For fusion, a CNN-based siamese network with Ratio-of-Laplacian pyramid method was used for the images acquired over the entire winter cropping period. This fusion strategy leads to better interpretability of results and also found that 2D CNN-based DL classifier performed well in terms of classification accuracy for both single-month (95.14% and 96.11%) as well as multi-temporal (99.87% and 99.91%) fusion in comparison to the SVM with classification accuracy for single-month (80.02% and 81.36%) and multi-temporal fusion (95.69% and 95.84%). Results indicate better performance by Vertical-Vertical polarised fused images than Vertical-Horizontal polarised fused images. Thus, implying the need to analyse classified images obtained by DL classifiers along with the classification accuracy.

KEYWORDS:

• Fusion
• Convolutional Neural Network (CNN)
• Support Vector Machine (SVM)
• Bayesian Optimisation

Acknowledgements

The First author is sincerely thankful to Space Applications Centre (SAC), Ahmedabad (India), for financial support under the project titled ‘Fusion of SAR and optical data’. The authors would like to acknowledge National Institute of Technology, Kurukshetra (India), for providing all the necessary institutional support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.