Generalized texture models for detecting high-level structures in remotely sensed images

Abstract

With the rapid increase in the amount and resolution of remotely sensed image data, automatic extraction and classification of information obtained from such images have been an important problem in the field of pattern recognition since remotely sensed imagery is a critical resource for diverse fields such as urban land use monitoring and management, GIS and mapping, environmental change and agricultural and ecological studies. This thesis proposes statistical and structural texture models for detecting high-level structures in remotely sensed images. The high-level structures correspond to complex geospatial objects with characteristic spatial layouts in a region. As opposed to the existing approaches that are based on classifying images using pixel level methods, we propose to use simple geospatial objects as textural primitives and exploit their spatial patterns. This representation can be viewed as a “generalized texture” measure where the image elements of interest are urban primitives instead of the traditional case of pixels. The spatial patterns we are interested in correspond to the regular and irregular arrangements of these primitives within neighborhoods. The methodology we propose in this thesis has two steps. First, the primitives of interest are detected using spectral, textural and morphological features with one-class classifiers. Then, the spatial patterns of these primitives are modeled. At this step, either a statistical or a structural approach can be followed. In the statistical approach, analysis of the spatial arrangement of the primitives is done by co-occurrence-based spatial domain features and Fourier spectrum-based frequency domain features. These features are used to quantify the likelihood of presence of the focused object in the image region being analyzed. In the structural approach, a graph-theoretic representation is proposed where the primitives form the nodes of a graph and the neighborhood information is obtained through Voronoi tessellation of the image scene. Next, the graph is clustered by thresholding its minimum spanning tree and the resulting clusters are classified as regular or irregular by examining the distributions of the angles between neighboring nodes. The algorithms proposed in this thesis are illustrated with the detection of two geospatial objects: settlement areas and harbors. The first step in the modeling of these objects is the detection of primitives such as buildings for settlement areas, and boats and water for harbors. In the second step, both statistical and structural approaches are illustrated for the modeling of the spatial patterns of these objects. Results of the experiments on high-resolution Ikonos satellite imagery and DOQQ aerial imagery show that the proposed techniques can be used for detecting the presence of geospatial objects in large remote sensing image datasets.