Application of a neural network to monitor river pollution index using spot satellite image

Min Luen Shih, Pao-Shan Yu, Terng Jou Wan, Po Jen Lin, Huei Ru Lin, Shih Peng Lo

研究成果: Conference contribution

摘要

Most successful studies on water quality monitoring by remote sensing mainly focused on lakes, reservoirs, estuaries, bays and oceans. Since neural networks have been widely applied to the nonlinear problem, therefore remotely monitoring river pollution indexes (RPI) using SPOT images could be a practical way. For atmospheric correction procedure Dark Object Subtraction was selected and followed by separating the samples into two groups for the seasonal variation. In order to consider the sampling difficulty on SPOT images with its limited pixel resolution, an unsupervised pre-classification with manual stream water sampling procedure were used for extracting the reliable water pixels from SPOT images. The study adopted artificial neural network (ANN) to examine and compare the predicting results of river pollution index in accordance with samples clustered by ungrouping and seasonal grouping. The results showed that if we grouped the samples in accordance with seasons, it would improve the accuracy of results to more than 90%. Overall accuracy of grouping simulation can still reach acceptable accuracy around 70% for smaller hidden nodes.

原文English
主出版物標題Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006
頁面1060-1065
頁數6
出版狀態Published - 2006 十二月 1
事件27th Asian Conference on Remote Sensing, ACRS 2006 - Ulaanbaatar, Mongolia
持續時間: 2006 十月 92006 十月 13

出版系列

名字Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006

Other

Other27th Asian Conference on Remote Sensing, ACRS 2006
國家/地區Mongolia
城市Ulaanbaatar
期間06-10-0906-10-13

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信

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