Modeling Of Spatial Strutural Features And Information Extraction For High Resolution Remote Sensing Images

High-resolution earth observation technology has obtained rapid development over the past decade, and at present, it has become one of the frontiers of high-tech development in the world. With a spatial resolution of meter or sub-meter level, high-resolution images can provide more detailed ground information and how to achieve the image interpretation and information extraction has become an important research topic. Spatial structural feature is one of the most significant spatia features of high resolution images, in which most of the object types exhibit different degrees of structural characteristics such as textural structure, geometrc structure and spatial relationships. It is well known that effectively utilizing spatial structural information can play an important role in impoving the image classfication result and increasing the object detection accuracy. In this paper, we studied the methods of spatial structural feature description, modeling and extraction based on spatial statistics and data field. The structural features or structural model obtained by the proposed methods were then applied to improving the image classification or achieving the detection of residential area class.The main work of this paper are as follows:(1) Discussed the concept, connotation and characteristics of spatial structural features in high resolution images. Drawing on the theory of system science which includes the cognition and understanding of structure, we recognized the spatial structure of an image from two aspects:one is the composed "element" of the image, and the other is the "spatial structural relationship" among these elements. Here the "element" is the basic unit of structure analysis, which is closely related to the spatial resolution of the image itself as well as human cognitive pattern of things. Taking into account the multi-scale characteristics of spatial structure, this paper proposed a multi-level analysis framework of spatial structure, which divided the spatial structure of high resolution images into three different levels including pixel structure, object structure and scene structure. In this framework, this paper focused on the first two levels of spatial structure, and studied the methods of spatial structural feature modeling based on spatial autocorrelation statistics, semi-variance function and data field, respectively. (2)Studied the methods of spatial structural feature description based on spatial autocorrelation statistics. First, we tested the response characteristics of spatial autocorrelation statistics to spatial dependance of different object classes in high resolution images. It was found that, Moran’s I and Getis statistics can make a good response to homogeneous regions in an image, and the latter can distinguish between homogeneous regions of high value and low value; Geary’s C statistic can make a strong response to heterogeneous regions or edges in the image. Further, according to intensity differences of spatial correlation in different regions in the image, this paper proposed a novel method of extracting homogeneous regions and edge structure from images using local spatial statistics. Second, the well-known six spatial autocorrelation statistics (including three global statistics and three local spatial statistics) were applied to the local spatial structural feature description and extraction of high resolution image, and the extracted spatial features were integrated with the spectral features for image classification. By comparing the classification results of using each statistic, the performance of each statistic was evaluated. Experimental results indicate that Moran’s I and Getis statistics can better improve the classification of homogeneous object classes, while Geary’s C statistic can significantly improve the classification of heterogeneous object classes (such as built-up areas and vegetation). It was also found that within the same neighborhood window, global statistics can get more spatial structural information but also need more computation cost than local statistics. Finally, to overcome some shortcomings of spatial autocorrelation statistics in the local spatial structure description, this paper proposed a novel method of modeling spatial structural features using directional spatial correlation(DSC). This method used eight directional half-lines instead of a window to measure spatial correlation in neighborhoods of pixels, which can take into account both the intensity and the length of spatial correlation. Compared with the usual window-ased methods, the proposed method can not only extract the information of spatial variability or surface texture but also can use the geometric information about shape and structure of object classes. In particular, it can also reduce the computation cost. By comparison, it was found the proposed method outperform some existing algorithms including Moran’s I, Geary’s C, GLCM, and showed competitive performance against wavelet transform based methods in accuracy or computation time.(3) Studied the methods of spatial structural feature modeling and residential area extraction based on based on semi-variance function. This paper first divided the common semi-variograms into three different types:"balanced","unbalanced" and "periodic", and then studied the spatial structural feature extraction method based on parameters characterizing semi-variogram curve shape. Then, a novel method of residential areas detection based on semi-variance function was proposed. This method used semi-variance function to modeling the spatial structure of residential areas, and it was found by experiments that the semi-variogram of residential areas is subject to the hole effect model with a certain period. Based on this model, this paper extracted feature parameters characterizing the spatial structure of residential areas, and further the extracted features were applied to the detection of residential areas.(4) Studied the methods of spatial structural feature modeling and residential area extraction based data field. First, this paper studied the relationship between the local spatial statistics in spatial statistics and the data field, and found that by selecting appropriate spatial weights, local spatial statistics can be unified into the potential function system of data field. In other words, local spatial statistics can be used as a potential function of data field, describing the interaction between data objects. Second, drawing on the method of local spatial statistics describing the spatial correlation, the data field method was applied to the spatial structural feature extraction of high resolution images, and the extracted features can be used to improve image classification. Finally, the data field method was used to describe the spatial structure of residential areas, based on which this paper proposed a novel method of residential area detection form high resolution images. This method regarded buildings or their partial structures (such as feature points, feature lines) in residential areas as mass points, and used potential functions of data field to describe the internal structure of residential areas; Then, according to potential value differences between the residential and non-residential areas, residential areas can be extraced from backgrounds by threshold segmentation, and the detected residential areas can be further improved by some post-processings such as "noise" removal, hole filling.