Abstract
Subsumed under the category of ocean colour (OC) data fusion tools, a new approach is developed to efficiently use the merits of two OC satellite sensors differing in their spatial and spectral resolution characteristics. The tool permits to combine high spectral but lower spatial resolution optical data from one satellite sensor with higher spatial resolution but lower spectral resolution data from the other one into an image possessing simultaneously both high spectral and high spatial resolution qualities. The developed algorithm employs the artificial intelligence tool: emulated/artificial neuron networks (AANs). The developed ANN algorithm performance and efficiency are demonstrated for Lake Michigan. The fusion was effected making use of multiband data from Sentinel-2 Multispectral Instrument (MSI) and MODIS-Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. In this version MODIS-Aqua sensor is chosen as an analog of the Sentinel 3 OLCI, whose spectrometric and atmospheric corrected data are yet unavailable. The multi-sensor (MS) optical-optical fusion results have persuasively demonstrated the efficiency of the approach and its applicability to studies of natural water bodies of different optical complexity. It can be utilized in combination with any biogeochemical retrieval algorithms. In the case of retrieving water quality parameters (WQP) in optically shallow aquatic environments, the employment of the fusion tool developed is particularly promising as the bottom reflectance properties are frequently highly heterogeneous. Indeed, in such cases, remote sensing optical data acquired at simultaneously high spatial and spectral resolution are certainly more advantageous as compared to that acquired separately by two different sensors operating either at high spatial (but low spectral) or high spectral (but low spatial) resolution. For the retrieval of WQP in optically shallow waters (OSW) a special algorithm called Biooptical Retrieval Algorithm (BOREALI) - OSW was applied to study the eastern coastal zone of Lake Michigan. The application of both the OC fusion tool and our BOREALI-OSW algorithm permitted to document both interannual dynamics in WQPs as well as bottom substrate spatial heterogeneity in the target OSW area of Lake Michigan.
Highlights
Known as a process of combining two or more different images into a single one, image fusion is intended to generate a new image carrying refined/ improved information sought for by researchers.According to the needs of the latter, image fusion is performed at three processing levels, viz., pixel, feature, and decision levels [Pohl and van Genderen, 1998].High-level fusion, i. e. feature level and decision level fusion is a multi-source data fusion that employs certain combinations of data sources of various nature being dictated by specific aims
In what follows we show that in the present form the developed procedure is of the hybrid family: belonging ideologically to the component substitution (CS) cohort of fusion methods, it exploits artificial neural networks (ANNs) to inject high spatial resolution features into a higher spec
The RGB images developed from the fused S-2a and Moderate Imaging Spectroradiometer (MODIS)-Aqua data exhibit a logical sequence
Summary
Known as a process of combining two or more different images into a single one, image fusion is intended to generate a new image carrying refined/ improved information sought for by researchers.According to the needs of the latter, image fusion is performed at three processing levels, viz., pixel, feature, and decision levels [Pohl and van Genderen, 1998].High-level fusion, i. e. feature level and decision level fusion is a multi-source data fusion that employs certain combinations of data sources of various nature being dictated by specific aims. Known as a process of combining two or more different images into a single one, image fusion is intended to generate a new image carrying refined/ improved information sought for by researchers. According to the needs of the latter, image fusion is performed at three processing levels, viz., pixel, feature, and decision levels [Pohl and van Genderen, 1998]. E. feature level and decision level fusion is a multi-source data fusion that employs certain combinations of data sources of various nature being dictated by specific aims. G. texture parameters) from different data sources to further combine them into one or more feature maps Feature level fusion extracts various features (e. g. texture parameters) from different data sources to further combine them into one or more feature maps
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