ABSTRACT Image fusion combines the images of different spectral, spatial, multi-date, as well as radiometric data to achieve a better quality image for improved classification results. Recently, Convolution Neural Network (CNN)-based classification algorithms are extensively used for remote sensing applications. Keeping this in view, present work proposes to use CNN-based fusion and classification of Sentinel 1 (VV and VH polarization) and Sentinel 2 datasets acquired over an agricultural area near Hisar (India). For image fusion, three CNN-based approaches are used to fuse Sentinel 2 (10 m) data with VV and VH bands of Sentinel 1 data. After fusion, classification was performed using 2D-CNN classifier to judge the performance of fused images in terms of classification accuracy. Results suggest that out of the three fusion approaches, only infrared image fusion (IVF) approach performed well with the considered dataset in terms of fusion indicators and classification accuracy. Keeping in view of its better performance, this study proposes a modified IVF approach by using different image pyramid methods. Comparison of results suggests an improved performance by modified IVF approach for the fusion of Sentinel 2 and Sentinel 1 data in comparison with the original IVF approach.

中文翻译：

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基于CNN的合成孔径雷达和光学数据的融合和分类

摘要 图像融合将不同光谱、空间、多日期和辐射数据的图像结合起来，以获得更好质量的图像，从而改善分类结果。最近，基于卷积神经网络 (CNN) 的分类算法被广泛用于遥感应用。考虑到这一点，目前的工作建议使用基于 CNN 的融合和分类的哨兵 1（VV 和 VH 极化）和哨兵 2 数据集在希萨尔（印度）附近的农业区获得。对于图像融合，三种基于 CNN 的方法用于将 Sentinel 2 (10 m) 数据与 Sentinel 1 数据的 VV 和 VH 波段融合。融合后，使用2D-CNN分类器进行分类，从分类精度方面判断融合图像的性能。结果表明，在三种融合方法中，只有红外图像融合 (IVF) 方法在融合指标和分类精度方面对所考虑的数据集表现良好。鉴于其更好的性能，本研究通过使用不同的图像金字塔方法提出了一种改进的 IVF 方法。结果的比较表明，与原始 IVF 方法相比，改进的 IVF 方法在融合 Sentinel 2 和 Sentinel 1 数据方面的性能有所提高。