| Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China | |
| Yu, Xiang2; Shao, Hong-bo1,3,4; Liu, Xiang-hua5; Zhao, Dong-zhi6 | |
| 发表期刊 | CLEAN-SOIL AIR WATER
 |
| ISSN | 1863-0650 |
| 2010-03-01 | |
| 卷号 | 38期号:3页码:289-295 |
| 关键词 | Environmental Quality Monitoring Hyper-spectrum Nerve Network Image Classification Landscape Characteristics |
| 产权排序 | [Shao, Hong-bo] QUST, Inst Life Sci, Qingdao 266042, Peoples R China; [Yu, Xiang] Binzhou Univ, Shandong Prov Key Lab Ecoenvironm Sci Yellow Rive, Binzhou, Peoples R China; [Shao, Hong-bo] Chinese Acad Sci, Inst Soil & Water Conservat, Yangling, Peoples R China; [Shao, Hong-bo] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China; [Liu, Xiang-hua] 11 Middle Sch Yantai, Yantai, Peoples R China; [Zhao, Dong-zhi] Natl Marine Environm Monitoring Ctr, Dalian, Peoples R China |
| 通讯作者 | Shao, HB, QUST, Inst Life Sci, Qingdao 266042, Peoples R China |
| 作者部门 | 滨海湿地实验室 |
| 英文摘要 | The spectral characteristics of mangroves on the Beihai Coast of Guangxi, P. R. China are acquired on the basis of spectral data from field measurements. Following this, the 3-layer reverse-conversing neural networks (NN) classification technology is used to analyze the Landsat TM5 image obtained on January 8, 2003. It is detailed enough to facilitate the introduction of the algorithm principle and trains project of the neural network. Neural network algorithms have characteristics including large-scale data handling and distributing information storage. This research firstly analyzes the necessity and complexity of this translation system, and then introduces the strong points of the neural network. Processing mangrove landscape characteristics by using neural network is an important innovation, with great theoretical and practical significance. This kind of neural network can greatly improve the classification accuracy. The spatial resolution of Landsat TM5 is high enough to facilitate the research, and the false color composite from 3-, 4-, and 5-bands has a clear boundary and provides a significant quantity of information and effective images. On the basis of a field survey, the exported layers are defined as mangrove, vegetation, bare land, wetlands and shrimp pool. TM satellite images are applied to false color composites by using 3-, 4-, and 5-bands, and then a supervised classification model is used to classify the image. The processing method of hyper-spectrum remote sensing allows the spectral characteristics of the mangrove to be determined, and integrates the result with the NN classification for the false color composite by using 3-, 4-, and 5-bands. The network model consists of three layers, i.e., the input layer, the hidden layer, and the output layer. The input layer number of classification is defined as 3, and the hidden layers are defined as 5 according to the function operation. The control threshold is 0.9. The training ratio is 0.2. The maximum permit error is 0.08. The classification precision reaches 86.86%. This is higher than the precision of maximal parallel classification (50.79%) and the spectrum angle classification (75.39%). The results include the uniformity ratio (1.7789), the assembly ratio (0.6854), the dominance ratio ( - 1.5850), and the fragmentation ratio (0.0325).; The spectral characteristics of mangroves on the Beihai Coast of Guangxi, P. R. China are acquired on the basis of spectral data from field measurements. Following this, the 3-layer reverse-conversing neural networks (NN) classification technology is used to analyze the Landsat TM5 image obtained on January 8, 2003. It is detailed enough to facilitate the introduction of the algorithm principle and trains project of the neural network. Neural network algorithms have characteristics including large-scale data handling and distributing information storage. This research firstly analyzes the necessity and complexity of this translation system, and then introduces the strong points of the neural network. Processing mangrove landscape characteristics by using neural network is an important innovation, with great theoretical and practical significance. This kind of neural network can greatly improve the classification accuracy. The spatial resolution of Landsat TM5 is high enough to facilitate the research, and the false color composite from 3-, 4-, and 5-bands has a clear boundary and provides a significant quantity of information and effective images. On the basis of a field survey, the exported layers are defined as mangrove, vegetation, bare land, wetlands and shrimp pool. TM satellite images are applied to false color composites by using 3-, 4-, and 5-bands, and then a supervised classification model is used to classify the image. The processing method of hyper-spectrum remote sensing allows the spectral characteristics of the mangrove to be determined, and integrates the result with the NN classification for the false color composite by using 3-, 4-, and 5-bands. The network model consists of three layers, i.e., the input layer, the hidden layer, and the output layer. The input layer number of classification is defined as 3, and the hidden layers are defined as 5 according to the function operation. The control threshold is 0.9. The training ratio is 0.2. The maximum permit error is 0.08. The classification precision reaches 86.86%. This is higher than the precision of maximal parallel classification (50.79%) and the spectrum angle classification (75.39%). The results include the uniformity ratio (1.7789), the assembly ratio (0.6854), the dominance ratio ( - 1.5850), and the fragmentation ratio (0.0325). |
| 文章类型 | Article |
| 资助机构 | Dalian Marine University ; National Marine Environment Monitoring Center, Dalian, P. R. China ; Chinese Academy of Sciences (CAS) |
| 收录类别 | SCI |
| 语种 | 英语 |
| 研究领域[WOS] | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| WOS记录号 | WOS:000276322000011 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.yic.ac.cn/handle/133337/3677 |
| 专题 | 中国科学院海岸带环境过程与生态修复重点实验室_滨海湿地实验室 |
| 作者单位 | 1.QUST, Inst Life Sci, Qingdao 266042, Peoples R China 2.Binzhou Univ, Shandong Prov Key Lab Ecoenvironm Sci Yellow Rive, Binzhou, Peoples R China 3.Chinese Acad Sci, Inst Soil & Water Conservat, Yangling, Peoples R China 4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 5.11 Middle Sch Yantai, Yantai, Peoples R China 6.Natl Marine Environm Monitoring Ctr, Dalian, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Xiang,Shao, Hong-bo,Liu, Xiang-hua,et al. Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China[J]. CLEAN-SOIL AIR WATER,2010,38(3):289-295. |
| APA | Yu, Xiang,Shao, Hong-bo,Liu, Xiang-hua,&Zhao, Dong-zhi.(2010).Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China.CLEAN-SOIL AIR WATER,38(3),289-295. |
| MLA | Yu, Xiang,et al."Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China".CLEAN-SOIL AIR WATER 38.3(2010):289-295. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Applying Neural Netw（1598KB） | 开放获取 | -- | 浏览 下载 | |||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论