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首页> 外文学位 >An unsupervised hierarchical clustering image segmentation and an adaptive image reconstruction system for remote sensing.
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An unsupervised hierarchical clustering image segmentation and an adaptive image reconstruction system for remote sensing.

机译：用于遥感的无监督分层聚类图像分割和自适应图像重建系统。

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摘要

The evolution of technology is radically affecting the quantity and quality of data collected in many scientific disciplines. This is especially true in the disciplines which contribute to the earth-science data information system due to innovations in "digital remote sensing." Remote sensing is a general term which includes aerial surveys and sonar and radar mapping, but which is primarily becoming applied to digital image data from satellites.; Recently, there has been increasing interest in the use of statistical methods to analyze the highly structured data of digital images. Statistical approaches in image processing have close ties with multivariate analysis and decision theory. This study is concerned with statistically-based image analysis, principally for applications in remote sensing.; A multistage algorithm which makes use of spatial contextual information in a hierarchical clustering procedure has been developed for unsupervised image segmentation. A Markov random field model is employed to enforce local spatial smoothness, while the maximum entropy principle is utilized to quantify global smoothness in the image. A multi-window approach implemented in a pyramid-like data structure which uses a so-called boundary blocking operation is employed to increase computational efficiency. The Schwarz information criterion is suggested as a means of selecting the level in the clustering hierarchy which corresponds to the optimal state.; An adaptive reconstruction system has also been developed to analyze sequential images observed at regular time intervals. A least-squares linear predictor with escalator structure has been implemented in the new system. Using the predictor, estimates of missing data or bad (possible cloud covered) data and the spatial parameters at a specified time can be estimated based on previous history. This algorithm recovers from observations which are contaminated due to blurring and/or correlated noise using temporally adapted spatial parameters. The reconstruction system can be used either individually for improving the pictorial information in the image or as a preprocessor for the image segmentation algorithm.; The segmentation procedure and the reconstruction system have been evaluated extensively using simulated data and applied to remotely sensed images from NOAA-n satellites.
机译：技术的发展正在从根本上影响许多科学学科中收集的数据的数量和质量。由于“数字遥感”的创新，在为地球科学数据信息系统做出贡献的学科中尤其如此。遥感是一个总称，包括航空测量，声纳和雷达测绘，但主要用于卫星的数字图像数据。近来，人们对使用统计方法来分析数字图像的高度结构化数据越来越感兴趣。图像处理中的统计方法与多元分析和决策理论密切相关。这项研究涉及基于统计的图像分析，主要用于遥感中。已经开发了一种在分级聚类过程中利用空间上下文信息的多阶段算法，用于无监督图像分割。马尔可夫随机场模型用于增强局部空间平滑度，而最大熵原理用于量化图像中的全局平滑度。采用以所谓的边界阻塞操作的类金字塔数据结构实现的多窗口方法来提高计算效率。建议使用Schwarz信息准则作为选择聚类层次结构中与最佳状态相对应的级别的一种方法。还开发了一种自适应重建系统来分析以规则时间间隔观察到的连续图像。在新系统中已实现了具有自动扶梯结构的最小二乘线性预测器。使用预测器，可以基于先前的历史记录来估计在指定时间丢失的数据或不良（可能被云覆盖）的数据以及空间参数的估计。该算法使用时间适应的空间参数从由于模糊和/或相关噪声而被污染的观测值中恢复。重建系统既可以单独用于改善图像中的图像信息，也可以用作图像分割算法的预处理器。分割程序和重建系统已使用模拟数据进行了广泛评估，并应用于NOAA-n卫星的遥感图像。

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