Doctoral Dissertation Research: A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification

博士论文研究：纹理辅助城市土地利用/土地覆盖分类的遗传贝叶斯方法

• 批准号: 0726512
• 负责人: Nina Lam
• 金额: $ 1.18万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0726512&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Genetic; Bayesian

The increased availability of high-resolution satellite imagery presents many new opportunities for detailed classification of urban land use and land cover, but it also brings to light the inadequacies of traditional spectral-based classification methods using the maximum-likelihood classifier. Recent research in remote sensing tends to focus on developing new textural methods and alternative classifiers to address this problem, but the potentially promising possibility of optimizing the maximum-likelihood classifier and combining multiple textural characteristics for image classification remains unexplored. Moreover, as an important parameter in texture analysis, the appropriate size of moving window (critical window) used to extract different textural layers and its association with land-use and/or land-cover objects has generated a lot of research, but the quantitative principles remain unclear. The overall goal of this doctoral dissertation research project is to develop an efficient, simple, and robust approach for image classification that has potential for automation. The doctoral candidate's objectives are (1) to develop and test a genetic Bayesian classifier to efficiently boost the performance of the traditional maximum-likelihood classifier by optimizing the prior probabilities with a genetic algorithm; (2) to determine whether a combination of textural indices, such as fractal dimension, lacunarity, and Moran's I, is efficient in classifying heterogeneous urban area, using the genetic Bayesian classifier; and (3) to derive the approximate size of the "critical window" when dealing with these texture indices and high-resolution IKONOS imagery. This project aims to improve urban land-use/land-cover classification accuracy by applying the new approach to high-resolution IKONOS pre- and post-Katrina imagery in New Orleans.The results of this study will provide a new method to improve land-use/land-cover classification through addressing cutting edge issues like genetic algorithm, Bayesian approach, critical window, fractal geometry, and spatial autocorrelation. The analysis of critical window and texture vector will provide better understanding of the relationships between the texture measures, land-use objects, and sensor resolutions, all of which are critical to the success of texture-aided classification. The emphasis on the development of a simple, quick, and reliable classification method is imperative for future automation and many applications such as rapid monitoring of disastrous and special events. The study area, New Orleans, was severely inundated by Hurricane Katrina in 2005. This project aims to provide a better post-disaster assessment with high-resolution satellite imagery that could help in guiding the recovery and urban planning. 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.

高分辨率卫星图像可用性的增加为城市土地利用和土地覆盖的详细分类提供了许多新的机会，但也暴露了使用最大似然分类器的传统基于光谱的分类方法的不足之处。 最近的遥感研究往往侧重于开发新的纹理方法和替代分类器来解决这个问题，但优化最大似然分类器和组合多个纹理特征进行图像分类的潜在前景仍未得到探索。 此外，作为纹理分析中的一个重要参数，用于提取不同纹理层的移动窗口（临界窗口）的适当大小及其与土地利用和/或土地覆盖对象的关联已经产生了大量的研究，但定量原则尚不明确。 该博士论文研究项目的总体目标是开发一种高效、简单且稳健的具有自动化潜力的图像分类方法。 该博士生的目标是（1）开发和测试遗传贝叶斯分类器，通过遗传算法优化先验概率，有效提高传统最大似然分类器的性能； (2) 使用遗传贝叶斯分类器，确定分形维数、空隙度和 Moran's I 等纹理指标的组合是否能够有效地对异质城市区域进行分类； (3) 导出处理这些纹理索引和高分辨率 IKONOS 图像时“关键窗口”的大致大小。 该项目旨在通过对新奥尔良卡特里娜飓风前后的高分辨率 IKONOS 图像应用新方法来提高城市土地利用/土地覆盖分类的准确性。这项研究的结果将为改善土地利用/土地覆盖分类提供一种新方法。通过解决遗传算法、贝叶斯方法、临界窗口、分形几何和空间自相关等前沿问题来进行利用/土地覆盖分类。 对关键窗口和纹理向量的分析将有助于更好地理解纹理测量、土地利用对象和传感器分辨率之间的关系，所有这些对于纹理辅助分类的成功至关重要。 对于未来的自动化和许多应用（例如灾难和特殊事件的快速监控）来说，开发简单、快速和可靠的分类方法势在必行。 研究区域新奥尔良在 2005 年遭受卡特里娜飓风严重淹没。该项目旨在利用高分辨率卫星图像提供更好的灾后评估，帮助指导灾后恢复和城市规划。 作为博士论文研究改进奖，该奖项还将为有前途的学生建立强大的独立研究生涯提供支持。