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

M. A. Syariz, Chao Hung Lin, A. C. Blanco

研究成果: Conference article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)401-405
頁數5
期刊International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
42
發行號4/W19
DOIs
出版狀態Published - 2019 十二月 23
事件2019 Geomatics and Data Science: Towards Adaptive Management in a Changing World, PhilGEOS x GeoAdvances 2019 - Manila, Philippines
持續時間: 2019 十一月 142019 十一月 15

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 地理、規劃與發展

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