Isometric mapping algorithm based GNSS-R sea ice detection

Yuan Hu; Zhihao Jiang; Xintai Yuan; Xifan Hua; Wei Liu; Yuan Hu; Zhihao Jiang; Xintai Yuan; Xifan Hua; Wei Liu

doi:10.3934/mina.2024002

Metascience in Aerospace

2024, Volume 1, Issue 1: 38-52. doi: 10.3934/mina.2024002

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Research article

Isometric mapping algorithm based GNSS-R sea ice detection

1.
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2.
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

Received: 14 June 2023 Revised: 22 July 2023 Accepted: 01 August 2023 Published: 14 August 2023

Global navigation satellite system reflectometry (GNSS-R) is based on satellite signals' multipath interference effect and has developed as one of the important remote sensing technologies in sea ice detection. An isometric mapping (ISOMAP)-based method is proposed in this paper as a development in sea ice detection approaches. The integral delay waveforms (IDWs), selected from February to April in 2018, derived from TechDemoSat-1 (TDS-1) Delay-Doppler maps (DDMs) are applied to open water and sea ice classification. In the first, the model for extracting low-dimensional coordinates of IDWs employs the randomly selected 187666 IDW samples, which are 30% of the whole IDW dataset. Then, low-dimensional coordinates of IDWs are used to train three different classifiers of support vector machine (SVM) and gradient boosting decision tree (GBDT), linear discriminant algorithm (LDA) and K-nearest neighbors (KNN) for determining the sea ice and sea water. The remaining 437889 samples, about 70% of the whole datasets, are used to validate with the ground surface type from the National Snow and Ice Data Center (NSIDC) data provided by the National Oceanic and Atmospheric Administration (NOAA). The algorithm performance is evaluated, and the overall accuracy of SVM, GBDT, LDA and KNN are 99.44%, 85.58%, 91.88% and 98.82%, respectively. The sea ice detection methods are developed, and the accuracy of detection is improved in this paper.
- GNSS-R,
- Sea ice detection,
- IDW,
- ISOMAP,
- classification
Citation: Yuan Hu, Zhihao Jiang, Xintai Yuan, Xifan Hua, Wei Liu. Isometric mapping algorithm based GNSS-R sea ice detection[J]. Metascience in Aerospace, 2024, 1(1): 38-52. doi: 10.3934/mina.2024002

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Abstract

Global navigation satellite system reflectometry (GNSS-R) is based on satellite signals' multipath interference effect and has developed as one of the important remote sensing technologies in sea ice detection. An isometric mapping (ISOMAP)-based method is proposed in this paper as a development in sea ice detection approaches. The integral delay waveforms (IDWs), selected from February to April in 2018, derived from TechDemoSat-1 (TDS-1) Delay-Doppler maps (DDMs) are applied to open water and sea ice classification. In the first, the model for extracting low-dimensional coordinates of IDWs employs the randomly selected 187666 IDW samples, which are 30% of the whole IDW dataset. Then, low-dimensional coordinates of IDWs are used to train three different classifiers of support vector machine (SVM) and gradient boosting decision tree (GBDT), linear discriminant algorithm (LDA) and K-nearest neighbors (KNN) for determining the sea ice and sea water. The remaining 437889 samples, about 70% of the whole datasets, are used to validate with the ground surface type from the National Snow and Ice Data Center (NSIDC) data provided by the National Oceanic and Atmospheric Administration (NOAA). The algorithm performance is evaluated, and the overall accuracy of SVM, GBDT, LDA and KNN are 99.44%, 85.58%, 91.88% and 98.82%, respectively. The sea ice detection methods are developed, and the accuracy of detection is improved in this paper.

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