International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 401–405, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 401–405, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

M. A. Syariz1, C.-H. Lin1, and A. C. Blanco2 M. A. Syariz et al.
  • 1Department of Geomatics, National Cheng Kung University, Taiwan
  • 2Department of Geodetic Engineering, University of the Philippines, Philippines

Keywords: chlorophyll-a, estimation, convolutional neural network, Sentinel-3, Laguna Lake

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.