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

城市土地利用、土地覆被变化和全球环境变化之间动态和复杂的相互作用的重要性已得到公认。 然而，尽管地理信息科学和技术取得了重大进展，但将数字遥感数据有效地分类为详细的城市土地类别仍然是一项挑战。 本研究旨在开发一种基于频率的多尺度分类算法，使用过完备小波变换，可以生成更高级别的对象和功能的空间安排，详细的城市土地分类。 研究人员将寻求通过添加分解程序来增强描述空间关联，空间模式，空间回归和隔离的空间建模和概念，这些分解程序可以在无限尺度上提取不同方向的空间特征。 选定的小波变换中使用的项目包括一系列的Daubechies和Coiflets。 该项目的多方面方法将允许新算法用于任何级别的规模，从大规模航空照片到粗分辨率MODIS和AVHRR数据。 将使用五种不同的归一化过程来防止大范围特征主导距离测量。 最小距离分类器的性能将使用子图像的计算的纹理特征值进行纹理分类评估。 此外，Mahalanobis距离规则将用于解释类别的可变性。 最后，将开发三个分类决策规则。 预计该项目将产生一个新的基于小波的框架，为地球物体在无限尺度上不同方向的空间特性和频率的作用提供科学证据。 这一框架和算法将公开提供，以便对遥感图像进行有效的图像分类，这一项目将使研究人员、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。 预计将生成更准确的地图，从而改进绘图和分析程序。 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功能的，可应用于各种其他土地覆盖、土地利用类型（即，农业、牧场、森林、湿地和沿海地区）和条件（即，干旱、火灾燃料浓度、荒漠化、洪水风险和海岸侵蚀），从而使其不仅对城市规划非常有用。此外，它还将通过为该领域的方法创新奠定基础来推进地理信息科学。

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.