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Mangrove Species Mapping Using Deep Learning with Fusion of Hyperspectral and High-Resolution Multispectral Images | IEEE Conference Publication | IEEE Xplore

Mangrove Species Mapping Using Deep Learning with Fusion of Hyperspectral and High-Resolution Multispectral Images


Abstract:

Accurate mapping of mangroves species is essential for mangrove management, and deep learning of hyperspectral images (HSIs) shows a great advantage in classification wit...Show More

Abstract:

Accurate mapping of mangroves species is essential for mangrove management, and deep learning of hyperspectral images (HSIs) shows a great advantage in classification with the fine spectrum. However, the sparely available annotations of HSIs are key challenges for accurate mapping using deep learning, especially for mangrove species within small patches. In this work, a high spatial resolution HSI is synthesized using the method of hyperspectral-multispectral image fusion with spectral variability, providing augmented samples as well as spatial information of mangroves. Secondly, the latest 3D convolutional neural network (3DCNN) was investigated to explore spatial and spectral information for mangrove species mapping. Compared to Gaofen 5 using conventional machine learning methods, the synthetic image provides manyfold samples and higher accuracy for mangrove species mapping using 3DCNNs. This work is expected to improve the situation of sample shortage and spatial information deficiency for mangrove species mapping using deep learning with HSIs.
Date of Conference: 11-16 July 2021
Date Added to IEEE Xplore: 12 October 2021
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Conference Location: Brussels, Belgium

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References

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