机构

• 成都山地灾害与环境研... [1]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [2]

内容类型

• 会议论文 [1]
• 期刊论文 [1]

发表日期

• 2016 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共2条，第1-2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 Biomass growth characteristics of 13-year-old Pinus densifloraS. et Z. in a post-fire plantation on different contour conditions in Samcheuk, Korea 期刊论文  OAI收割 Journal of Mountain Science, 2016, 卷号: 13, 期号: 7, 页码: 1238-1244 作者:  Lee Ju-Hyoung;  LeeDo-Hyung;  Kim Do-Hyun;  Park Jin-Hwa;  Kim Jae-Hee 收藏  |  浏览/下载：42/0  |  提交时间：2016/06/24 Pinus densiflora  Growth characteristics  Contour conditions  Planted stand  Post-fire plantation   Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文  OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Sun J.; Ma D. 收藏  |  浏览/下载：25/0  |  提交时间：2013/03/25 The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task  for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type  the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP  but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly  we use frame grabber to capture the 4-bar target image data. Then according to image gray scale  we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets  along with known target visibility  are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm  demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.