Object recognition and image segmentation: the Feature Analyst® approach
Object recognition and image segmentation: the Feature Analyst® approach
复制标题
DOI：
10.1007/978-3-540-77058-9_8
复制
发表时间：
2008
期刊：
影响因子：
64.5
通讯作者：
D. Opitz;S. Blundell
中科院分区：
文献类型：
--
作者：
D. Opitz;S. Blundell
在地理信息系统（GIS）的维护周期中，从高分辨率地球图像中收集特定对象的地理空间特征（如道路和建筑物）是一个耗时且昂贵的问题。传统的收集方法，如手工数字化，速度慢，繁琐，无法跟上不断增长的图像资产量。在本文中，我们描述了特征分析自动特征提取（AFE）软件的基本方法，该软件解决了GIS技术中的这一核心问题。Feature Analyst是一个领先的商业AFE软件系统，它提供了一套机器学习算法，可以实时学习如何对分析师指定的特定对象的特征进行分类。该软件在提取特征时使用空间上下文，并提供自然的分层学习方法，迭代地提高提取精度。自适应用户界面隐藏了底层机器学习系统的复杂性，同时为特征提取、编辑和归属提供了一套全面的工具。最后，系统将自动生成脚本，允许在额外的图像集上批量处理AFE模型，以支持大容量、地理空间和数据生产需求。
The collection of object-specific geospatial features, such as roads and buildings, from high-resolution earth imagery is a time-consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Traditional collection methods, such as hand-digitizing, are slow, tedious and cannot keep up with the ever-increasing volume of imagery assets. In this paper we describe the methodology underlying the Feature Analyst automated feature extraction (AFE) software, which addresses this core problem in GIS technology. Feature Analyst, a leading, commercial AFE software system, provides a suite of machine learning algorithms that learn on-the-fly how to classify object-specific features specified by an analyst. The software uses spatial context when extracting features, and provides a natural, hierarchical learning approach that iteratively improves extraction accuracy. An adaptive user interface hides the complexity of the underlying machine learning system while providing a comprehensive set of tools for feature extraction, editing and attribution. Finally, the system will automatically generate scripts that allow batch-processing of AFE models on additional sets of images to support large-volume, geospatial, data-production requirements.