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题目：Detection of Smoke in Satellite Images Using Autologistic Regression
嘉宾：Mark Wolters 博士 （加拿大西安大略大学统计与精算系）
时间：2014年4月18日下午4:15pm
地点：管理科研楼10楼1018会议室
单位：统计与金融系
摘要：
Satellite imagery provides a rich data source for studying the spread of smoke produced by forest fires. The present work considers hyperspectral images, where each of 36 image planes holds a measurement in a different spectral band. The objective is to develop an automatic image segmentation routine: a classifier that assigns a new image's pixels to either the "smoke" or "nonsmoke" category. To incorporate association between nearby pixels, the true scene is modeled as a binary random field. An autologistic regression approach is used to model the joint probability mass function of the scene, with the hyperspectral data as predictors. This model poses computational challenges for feature selection, estimation, and prediction; methods for overcoming these challenges will be discussed.
嘉宾：Mark Wolters 博士 （加拿大西安大略大学统计与精算系）
时间：2014年4月18日下午4:15pm
地点：管理科研楼10楼1018会议室
单位：统计与金融系
摘要：
Satellite imagery provides a rich data source for studying the spread of smoke produced by forest fires. The present work considers hyperspectral images, where each of 36 image planes holds a measurement in a different spectral band. The objective is to develop an automatic image segmentation routine: a classifier that assigns a new image's pixels to either the "smoke" or "nonsmoke" category. To incorporate association between nearby pixels, the true scene is modeled as a binary random field. An autologistic regression approach is used to model the joint probability mass function of the scene, with the hyperspectral data as predictors. This model poses computational challenges for feature selection, estimation, and prediction; methods for overcoming these challenges will be discussed.
