基于尺度集的高分辨率遥感影像多尺度分类

项目名称： 基于尺度集的高分辨率遥感影像多尺度分类

项目编号： No.41501369

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 胡忠文

作者单位： 深圳大学

项目金额： 20万元

中文摘要： 遥感平台和传感器技术快速发展，影像的空间分辨率显著提高，这为影像解译带来了新的挑战。基于对象的影像分析是高分遥感影像解译的主要方法，其发展远落后于硬件技术，关键在于难以解决分析尺度的问题。本课题拟以尺度集理论为指导，以影像多尺度分类为应用突破点，研究“基于区域的影像多尺度建模—基于知识的影像解译”的分析模式，为解决基于对象影像分析的尺度问题提供新的思路。本项目研究内容包括：首先研究顾及特征随尺度变化规律的遥感影像多尺度建模方法，实现区域的多尺度表达；然后研究尺度集框架下的区域特征挖掘与分类策略，实现区域的多尺度分类；最后研究区域的最优尺度计算模型，实现逐区域的最优尺度分类结果选择。课题提出基于尺度集的全新的影像解译模式，为尺度问题的解决开辟了新的途径。课题研究成果可有效提升高分辨率遥感影像解译效率和精度，挖掘影像价值，对推进高分辨率遥感影像在各领域的应用广度和深度有积极意义。

中文关键词： 基于对象的影像分析；监督分类；影像分割；尺度集；多尺度分析

英文摘要： With the rapid development of platforms and sensors of remote sensing, the spatial resolution of images significantly improves. It brings new challenges to the interpretation of high-resolution (HR) remote sensing images. Object-based image analysis (OBIA) is the main method for HR image interpretation, but its development is far behind the development of hardware technology. The key lies in its difficulty in solving the scale problem. In this research, we introduce the scale-sets theory to OBIA, in order to better solve the problem. In scale-sets framework, an image is firstly represented using a region-based hierarchal model, and then a high-level image analysis can be employed, where the scale problem is solved. Furthermore, the scale-sets provides the full hierarchal relationship for multi-scale and optimal scale analysis of each individual region. This research contains three aspects: .1) Region-based multi-scale and hierarchical image representation. In this study, different image features and their variations along different scales will be considered for accurate modeling of regions appearing at different scales..2) Region-based feature extraction and classification in scale-sets framework. In this study, the low-level spectral, texture and spatial features are extracted; and furthermore, middle-level and high-level features are extracted by employing adjacency and hierarchical relationships in scale-sets framework; and then, all the regions appearing at different scales are classified using supervised machine learning approaches..3) Optimal scale analysis in scale-sets framework. In this study, a new model will be developed for optimal scale selection of each individual region, by analyzing the adjacency, hierarchical and class properties of regions, and then, the optimal classification result of the whole image is obtained..It is expected that the scale problem of OBIA can be better solved by analyzing images in scale-sets framework. And furthermore, the efficiency, accuracy and automation of OBIA can be significantly improved. This research therefore can help us to better take advantage of HR images, and promote the breadth and depth use of HR remote sensing images in various applications.

英文关键词： Object-based Image Analysis;Supervised Classification;Image Segmentation;Scale-sets;Multi-scale Analysis