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CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES
- Source :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4-W19, Pp 401-405 (2019)
- Publication Year :
- 2019
- Publisher :
- Copernicus Publications, 2019.
-
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.
- Subjects :
- lcsh:Applied optics. Photonics
010504 meteorology & atmospheric sciences
Pixel
Artificial neural network
Computer science
business.industry
lcsh:T
Feature extraction
lcsh:TA1501-1820
Pattern recognition
010501 environmental sciences
01 natural sciences
Convolutional neural network
lcsh:Technology
Dual (category theory)
lcsh:TA1-2040
Radiative transfer
Stage (hydrology)
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
0105 earth and related environmental sciences
Subjects
Details
- Language :
- English
- ISSN :
- 21949034 and 16821750
- Database :
- OpenAIRE
- Journal :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Accession number :
- edsair.doi.dedup.....ef1519bc416638d4b2abb49d2d62d433