Doctoral Dissertation Research: Detecting the Social-economic Conditions of Urban Neighborhoods Through a Combined Methodology of Wavelet Transform and Artificial Neural Networks

博士论文研究：通过小波变换和人工神经网络的组合方法检测城市社区的社会经济状况

• 批准号: 0602111
• 负责人: Nina Lam
• 金额: $ 0.71万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-15 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0602111&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Detecting; Social

Relating the socio-economic conditions of urban neighborhoods through remote sensing imagery has been a major research challenge in remote sensing. It is clear from the literature that conventional per-pixel classification methods are not efficient in accurately classifying urban neighborhoods from high-resolution imagery due to the heterogeneous nature of urban environment. Instead, classification methods that utilize the textural characteristics of the image could dramatically improve the classification accuracy. Whether these textural methods can be used to detect the socio-economic conditions of urban neighborhoods remains a key question that this project will address. The goals of this doctoral dissertation research project are threefold: 1) to determine whether neural networks in combination with wavelet analysis are effective for the characterization and classification of urban areas; 2) to determine whether texture patterns can be associated with the social-economic conditions of urban neighborhoods; and 3) to explore the effects of image resolution on the classification accuracy. Two study areas, Atlanta, Georgia and Baton Rouge, Louisiana, will be examined and compared, and high-resolution IKONOS images of the study areas will be used. Fieldwork will be carried out and census and other socio-economic data will be used. Geographic phenomena operate at different scales; hence a multi-resolution approach will be used. Wavelet analysis is an efficient approach to studying textural patterns at different scales, whereas artificial neural networks can learn very complex patterns in the data. A combined methodology that utilizes both methods is expected to outperform other classification methods.Although wavelet analysis and artificial neural networks have been employed separately to analyze remote sensing images, studies that bring them together are rare and the synergy is yet to be explored. This research will increase our knowledge of textures, texture methods, and their relationships with real features on the ground. The research will also enhance our appreciation of the scale effect on the classification of heterogeneous urban environment from high-resolution imagery. The identification and characterization of urban neighborhoods of different social-economic statuses holds the promises of opening up a new avenue to link remotely sensed images to the social-economic aspects of human activities. The methodology used in the project can be extended to study other social-economic applications such as detecting the sanitary or health conditions on the ground and to other classification scenarios (e.g., non-urban applications), hence will add significantly to the field of remote sensing, image processing, environmental assessment and monitoring, and urban analysis. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

通过遥感图像将城市社区的社会经济条件联系起来一直是遥感方面的重大研究挑战。 从文献中可以明显看出，由于城市环境的异质性质，传统的人均分类方法无法有效地从高分辨率图像中准确地对城市社区进行分类。 取而代之的是，利用图像的纹理特征的分类方法可以显着提高分类精度。这些纹理方法是否可以用于检测城市社区的社会经济状况仍然是该项目将解决的关键问题。 该博士学位论文研究项目的目标是三重：1）确定神经网络与小波分析的结合是否有效地对城市地区的表征和分类有效； 2）确定是否可以与城市社区的社会经济条件相关联； 3）探索图像分辨率对分类精度的影响。将检查和比较两个研究领域，分别是亚特兰大，乔治亚州和路易斯安那州的巴吞鲁日，将使用研究区域的高分辨率Ikonos图像。将进行现场工作，并将使用人口普查和其他社会经济数据。地理现象以不同的规模运行；因此，将使用多分辨率的方法。 小波分析是一种在不同尺度研究纹理模式的有效方法，而人工神经网络可以学习数据中非常复杂的模式。一种利用这两种方法的组合方法都可以胜过其他分类方法。尽管小波分析和人工神经网络已被分别用于分析遥感图像，但是将它们融合在一起的研究很少见，并且尚未探索协同作用。这项研究将增加我们对纹理，纹理方法及其与地面上真正特征的关系的了解。这项研究还将增强我们对从高分辨率图像对异质城市环境分类的规模影响的欣赏。对不同社会经济地位的城市社区的认同和表征具有开放新的途径，将远程感知的图像与人类活动的社会经济方面联系起来。 可以扩展项目中使用的方法来研究其他社会经济应用，例如检测地面上的卫生或健康状况以及其他分类场景（例如，非城市应用程序），因此将大大增加远程领域感应，图像处理，环境评估和监测以及城市分析。作为博士学位论文研究改进奖，该奖项还将提供支持，使有前途的学生能够建立强大的独立研究职业。