机构

• 长春光学精密机械与物... [2]
• 海洋研究所 [1]
• 生态环境研究中心 [1]

采集方式

• OAI收割 [4]

内容类型

• 会议论文 [2]
• 期刊论文 [2]

发表日期

• 2022 [1]
• 2016 [1]
• 2012 [2]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 The Structure of the Seasonal Benthic Diatom Community and Its Relationship With Environmental Factors in the Yellow River Delta 期刊论文  OAI收割 FRONTIERS IN MARINE SCIENCE, 2022, 卷号: 9, 页码: 10 作者:  Liu, Xing;  Han, Jichang;  Li, Yuhang;  Zhu, Baohua;  Li, Yun   |  收藏  |  浏览/下载：60/0  |  提交时间：2022/04/12 the Yellow River Delta  high-throughput sequencing  benthic diatom community structure  environment factors  redundancy analysis   Bacterial community compositions in sediment polluted by perfluoroalkyl acids (PFAAs) using Illumina high-throughput sequencing 期刊论文  OAI收割 ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2016, 卷号: 23, 期号: 11, 页码: 10556-10565 作者:  Sun, Yajun;  Wang, Tieyu;  Peng, Xiawei;  Wang, Pei;  Lu, Yonglong 收藏  |  浏览/下载：27/0  |  提交时间：2017/03/24 PFOA  Bacterial community structure  Illumina high-throughput sequencing  Redundancy analysis  Thiobacillus  Sulfurimona   On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文  OAI收割 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China 作者:  Yu P.;  Yu P.;  Gao X. 收藏  |  浏览/下载：20/0  |  提交时间：2013/03/25 As an effective statistical learning tool  AdaBoosting has been widely used in the field of pattern recognition. In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy  the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality  whilst to remove noise bands simultaneously. Then  we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.   An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Yu P.;  Yu P. 收藏  |  浏览/下载：31/0  |  提交时间：2013/03/25 In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA)  genetic algorithm (GA)  and artificial neural networks (ANNs). First  some characteristics of the hyperspectral remotely sensed data  such as high correlation  high redundancy  etc.  are investigated. Based on the above analysis  we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next  we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem  we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.