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.