Research On Urban And Building Detection From High Resolution Remotely Sensed Imagery

In the past few years, urban area and building detection from high-resolution remotely sensed image have become crucial for several applications. The main one is to update the geographic information databases, which are critical sources of information in diverse fields such as cartography, city planning and change detection. To this end, this dissertation is trying to propose several algorithms for urban and building detection from high resolution remotely sensed images. Concretely, main contents of this dissertation include the following four parts:Firstly, we briefly review previous works on feature extraction approaches, and divide them into three categories:spectral feature extraction, texture feature extraction and local feature extraction. Afterwards, we descible in detail the basic concept and principle of feature extraction approaches, which are commonly used in high-resolution image interpretation.Secondly, a supervised urban detection approach based on mutil-feature fusion model is proposed. In this method, we treat the problem of mutil-feature fusion as estimating a weighed linear combination of mutilple feature kernel functions. And the weight for each feature kernel function is automatically estimated in a multi-kernel support vector machine (SVM) learning framework during the training stage. In the classification stage, we first divide the test image into several non-overlapping image blocks, and then apply the SVM classifier to determine whether each image block belongs to urban or not. Compared to traditional approaches using only texture information for urban detection, experimental results demonstrate that fusing multiple features can help improving urban detection accuracy rate.Thirdly, given a set of high resolution satellite images covering different scenes, an unsupervised approach to simultaneously detect possible urban regions from them is proporsed. The motivation behind is that:the frequently recurring appearance patterns or repeated textures corresponding to common objects of interest (e.g. urban area) in the input image dataset can help us discriminate urban area from others. With this inspiration, our method consists of two steps. First, we extract a large set of local feature point by Harris corner detector. In order to achieve a reliable extraction of corners from urban areas, we further propose two criterions to validate and filter them. Afterwards, we incorporate the extracted corners into a likelihood function to locate candidate regions in each input images. Given a set of candidate urban regions, in the second stage, we formulize the urban detection process as an unsupervised classification problem. The candidate regions are modeled through their histogram representation of Gabor texture features, and the classification problem is solved by spectrum clustering and graph cuts. The experimental results show that the proposed approach is capable of and efficient at simultaneously detecting urban regions from multiple high-resolution satellite images, and performs comparable or even better in comparison with the state-of-the-art supervised method.In high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by a recognition of more complicated ones by deduction. Inspired by this theory, a novel hierarchical building extraction framework is proposed to simulate the process, which includes three major components. Firstly, a total variation based segmentation algorithm is presented to decompose the given image into object-level elements. Then, shape analysis is applied to extract some common and easily identified rectangular buildings. To ensure each candidate of building target is isolated, a multidirectional morphological road-filtering algorithm is designed to separate the buildings from their neighboring roads with similar spectrum. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation, and a Bayesian based approach is put forward to deal with it. Comparing to the conventional way of detecting objects through the information learned from previously collected training samples, our method has two advantages. First, our approach can learn building models directly from the original images. Therefore, it is highly automatic, for no manual aid is required in the collection of training data. More importantly, since the training data are collected from the identical scale and illumination conditions (e.g., in the same image), our model is more discriminating. This enables that the proposed framework has the ability to detect building with complex structures and varying spectral response, independent of pre-defined and limited building models.