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

通过遥感图像关联城市社区的社会经济状况一直是遥感领域的一个主要研究挑战。 从文献中可以清楚地看出，由于城市环境的异质性，传统的每像素分类方法在从高分辨率图像中准确地对城市街区进行分类方面效率不高。 相反，利用图像的纹理特征的分类方法可以显着提高分类精度。这些纹理的方法是否可以用来检测城市社区的社会经济状况仍然是一个关键问题，这个项目将解决。 本博士论文研究项目的目标有三个方面：1）确定神经网络与小波分析相结合是否对城市地区的表征和分类有效; 2）确定纹理模式是否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。两个研究领域，亚特兰大，格鲁吉亚和巴吞鲁日，路易斯安那州，将进行检查和比较，高分辨率IKONOS图像的研究领域将被使用。将进行实地工作，并将使用普查和其他社会经济数据。地理现象在不同的尺度上发生作用，因此将采用多分辨率方法。 小波分析是研究不同尺度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。虽然小波分析和人工神经网络已分别用于遥感图像分析，但将它们结合起来的研究很少，协同作用还有待探索。这项研究将增加我们的纹理，纹理方法，以及它们与地面上的真实的特征的关系的知识。这项研究也将提高我们的欣赏尺度效应的分类异质城市环境从高分辨率图像。对不同社会经济状况的城市街区进行识别和定性，有望开辟一条新的途径，将遥感图像与人类活动的社会经济方面联系起来。 该项目中使用的方法可以扩展到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方案（例如，非城市应用），因此将大大增加遥感、图像处理、环境评估和监测以及城市分析领域。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。

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.