An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery
文献类型：期刊论文
| 作者 | Pyo, JongCheol; Duan, Hongtao; Ligaray, Mayzonee; Kim, Minjeong; Baek, Sangsoo; Kwon, Yong Sung; Lee, Hyuk; Kang, Taegu; Kim, Kyunghyun; Cha, YoonKyung |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 12期号:7 |
| 英文摘要 | Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash-Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application. |
| 源URL | [http://159.226.73.51/handle/332005/20170]  |
| 专题 | 中国科学院南京地理与湖泊研究所 |
| 推荐引用方式 GB/T 7714 | Pyo, JongCheol,Duan, Hongtao,Ligaray, Mayzonee,et al. An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery[J]. REMOTE SENSING,2020,12(7). |
| APA | Pyo, JongCheol.,Duan, Hongtao.,Ligaray, Mayzonee.,Kim, Minjeong.,Baek, Sangsoo.,...&Cho, Kyung Hwa.(2020).An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery.REMOTE SENSING,12(7). |
| MLA | Pyo, JongCheol,et al."An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery".REMOTE SENSING 12.7(2020). |
入库方式： OAI收割
来源：南京地理与湖泊研究所
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