| HU Yabin,ZHANG Jie,Ma Yi,LI Xiaomin,SUN Qinpei,AN Jubai. 2019. Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland. Acta Oceanologica Sinica, 38(5):142－150 |
| Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland |
| 联合光谱和纹理特征的滨海湿地高光谱深度学习分类—以黄河三角洲湿地为例 |
| Received:March 14, 2018 |
| DOI：10.1007/s13131-019-1445-z |
| Key words:coastal wetland hyperspectral image deep learning classification |
| 中文关键词: 滨海湿地 高光谱图像 深度学习 分类 |
| 基金项目:The National Natural Science Foundation of China under contract No. 61601133 and 41206172; the Marine Application System of High Resolution Earth Observation System Major Project. |
| Author Name | Affiliation | E-mail | | HU Yabin | Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China | | | ZHANG Jie | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China | | | Ma Yi | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China | mayimail@fio.org.cn | | LI Xiaomin | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China | | | SUN Qinpei | College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China | | | AN Jubai | Information Science and Technology College, Dalian Maritime University, Dalian 116026, China | |
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| Abstract: |
| This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe (Yellow) River Estuary coastal wetland. The results show that:(1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%. (2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%. (3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80. (4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72% respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods. |
| 中文摘要: |
| 本文基于CHRIS高光谱遥感影像，发展了一种结合地物光谱特征和多纹理空间特征信息，采用双全链接的8层深度卷积神经网络分类算法对滨海湿地高光谱影像进行遥感地物分类，并在黄河口滨海湿地进行了应用。结果表明：1）基于测试样本数据，联合光谱特征和K-L变换的纹理特征信息，采用DCNN模型方法展现了高的分类精度，精度高达99%；2）利用光谱特征和全纹理特征的精度比仅使用光谱特征和光谱特征联合K-L变换后纹理特征的分类精度低。利用K-L变换后的光谱特征和纹理特征的DCNN分类精度达到99.38%，相比于使用全纹理特征信息的精度提高了4.15%；3）基于验证图像，发展的DCNN分类方法精度优于其他算法，DCNN方法总体分类精度为84.64%，Kappa系数为0.80；4）相比于浅层分类方法，本文发展的DCNN模型分类算法保证了所有地物类型的分类精度更加均衡，保持了主要地物类型的分类精度几乎不变，同时提高了滩涂和农田的精度。基于DCNN模型，潮滩和农田的分类精度分别达到79.26%和56.72%。比其它浅层分类方法提高了2.51%和10.6%。 |
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