Abstract Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.

中文翻译：

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从复杂沿海和内陆水域的多光谱数据重建高光谱分辨率图像的新方法

摘要 合成传感器的高光谱分辨率图像产品具有星载传感器的高空间分辨率，可以提供具有成本效益的方法，以提高我们目前提供图像阵列的能力，而不是设计昂贵的图像采集系统，它可以满足科学界和用户界对各种关键水彩应用不断扩大的需求。尽管有一些关于提高陆地遥感传感器能力的研究，但由于缺乏方法和准确的大气校正程序，具有不同光谱带的水彩图像的全光谱重建受到阻碍。在目前的工作中，开发了一种新方法，用于从代表沿海和内陆地区许多复杂水域的高空间分辨率哨兵 2 多光谱仪器 (MSI) 数据重建高光谱分辨率图像。该方法使用具有多个解卷积块和密集层的深度神经网络 (DNN)。来自多光谱数据的高光谱分辨率图像的光谱重建基于来自不同水类型的大气校正和验证的 HICO 归一化离水辐射产品（光谱分辨率为 438-868 nm，在 5.7 nm 处采样）的严格训练数据。使用并行星载（MSI 和 HICO）和来自不同区域水域的原位测量，通过各种定性和定量分析，系统地测试和评估了 DNN 方法的通用性和通用性。从 MSI 图像中获得的重建高光谱分辨率辐射与所需精度内的独立 HICO 和 MSI 测量密切匹配。高光谱辐射的成功重建和验证表明，所提出的最先进方法为增强我们目前在地方、区域和全球范围内用于各种研究目的和社会应用的星载传感器能力提供了可能的未来方向。从 MSI 图像中获得的重建高光谱分辨率辐射与所需精度内的独立 HICO 和 MSI 测量密切匹配。高光谱辐射的成功重建和验证表明，所提出的最先进方法为增强我们目前在地方、区域和全球范围内用于各种研究目的和社会应用的星载传感器能力提供了可能的未来方向。从 MSI 图像中获得的重建高光谱分辨率辐射与所需精度内的独立 HICO 和 MSI 测量密切匹配。高光谱辐射的成功重建和验证表明，所提出的最先进方法为增强我们目前在地方、区域和全球范围内用于各种研究目的和社会应用的星载传感器能力提供了可能的未来方向。