Wavelet Analysis of High Spatial Resolution Imagery for Urban Mapping Using Infinite Scale Decomposition Techniques

使用无限尺度分解技术对城市测绘高空间分辨率图像进行小波分析

• 批准号: 1154904
• 负责人: Soe Win Myint
• 金额: $ 12.5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1154904&HistoricalAwards=false
• 关键词: Wavelet; Analysis; Spatial; Resolution; Imagery

The importance of dynamic and complex interactions among urban land use, land-cover change, and global environmental change is well recognized. Despite significant advances in geographic information science and technology, however, effectively categorizing digital remote sensing data into detailed urban land categories remains a challenge. This research project aims to develop a frequency-based, multi-scale classification algorithm using overcomplete wavelet transforms that can generate higher-level spatial arrangements of objects and features for detailed urban land categorization. The investigator will seek to enhance spatial modeling and concepts that describe spatial association, spatial pattern, spatial regression, and segregation by adding decomposition procedures that can extract spatial features in different directions at infinite scale. Selected wavelet transforms to be used in the project include a series of Daubechies and Coiflets. The project's multi-faceted approach will permit the new algorithm to be used for any level of scale, from large-scale air photos to coarse-resolution MODIS and AVHRR data. Five distinct normalization procedures will be used to prevent large-range features from dominating the distance measure. The performance of a minimum distance classifier will be evaluated for texture classification using the computed texture feature value of the sub-images. In addition, the Mahalanobis distance rule will be employed to account for the variability of classes. Finally, three classification decision rules will be developed. The project is expected to result in a new wavelet-based framework that provides scientific evidence for the role of spatial properties and frequencies of geo-objects in different directions at infinite scale. This framework and algorithm will be made publicly available for performing image classification effectively in remotely sensed imagery.This project will enable researchers, modelers, and analysts to differentiate among urban land-cover and land-use types and to categorize detailed urban land data. It is expected to generate maps that are more accurate, thereby improving mapping and analysis procedures. The new geospatial frequency-based framework and methods will help improve semi-automated and automated analysis procedures. The tools from this project will be versatile and can be applied to a wide variety of other land cover, land-use types (i.e., agriculture, rangeland, forest, wetlands, and coastal zones) and conditions (i.e., drought, fire fuel concentrations, desertification, flood risk, and coastal erosion), thereby making it very useful for more than just urban planning. Furthermore, it will advance geographic information science by building a foundation for methodological innovations in the field.

人们充分认识到城市土地利用、土地覆盖变化和全球环境变化之间动态而复杂的相互作用的重要性。然而，尽管地理信息科学和技术取得了重大进展，有效地将数字遥感数据分类为详细的城市土地类别仍然是一项挑战。该研究项目旨在开发一种基于频率的、使用过完备小波变换的多尺度分类算法，该算法可以生成对象和要素的更高层次的空间排列，以用于详细的城市土地分类。研究人员将寻求通过添加可以在无限尺度上提取不同方向的空间特征的分解过程来增强空间建模和描述空间关联、空间模式、空间回归和分离的概念。在该项目中使用的选定小波变换包括一系列Daubechies和Coiflet。该项目的多方面方法将允许新算法用于任何级别的比例尺，从大规模航空照片到粗分辨率MODIS和AVHRR数据。将使用五个不同的归一化过程来防止大范围特征主导距离度量。对于纹理分类，将使用子图像的计算纹理特征值来评估最小距离分类器的性能。此外，将采用马氏距离规则来解释班级的可变性。最后，开发了三个分类决策规则。该项目预计将产生一个新的基于小波的框架，为在无限尺度上不同方向的地球物体的空间特性和频率的作用提供科学证据。这个框架和算法将用于在遥感图像中有效地执行图像分类。该项目将使研究人员、建模人员和分析人员能够区分城市土地覆盖和土地利用类型，并对详细的城市土地数据进行分类。预计它将生成更准确的地图，从而改进测绘和分析程序。新的基于地理空间频率的框架和方法将有助于改进半自动和自动分析程序。该项目的工具将是多才多艺的，可以应用于各种其他土地覆盖、土地利用类型(即农业、牧场、森林、湿地和海岸带)和条件(即干旱、火灾燃料集中、荒漠化、洪水风险和海岸侵蚀)，从而使其不仅适用于城市规划。此外，它将通过为该领域的方法创新奠定基础，推动地理信息科学的发展。