Man-made Objects And Natural Scenes Classification By Partial Differential Equation

Classification of remote sensing image, which consists of assigning a label to each pixel of an observed image, has been one of key issues for remote sensing image analysis and understanding. Feature extraction and classification are two main steps in the classification procedure. In this paper, we concentrate on the following studies: feature extraction technology based on image multi-scale geometric analysis, remote sensing image classification method based on partial differential equations and the application of sparse classifier technology in the remote sensing image classification.Firstly, this paper presents the state of arts about aerial image classification, discusses the aerial image classification methods and the corresponding application fields. Then, the technologies of feature extraction are studied and each method is compared and evaluated. Finally, some algorithm of feature extraction and classification for remote sensing image are presented by considering the recent development and prograss of image processing and pattern recognition knowledge. In this thesis, we present some studies concentrated in the following topics:1. The wavelet transform is widely used in many fields, it can provide a very sparse representation for piecewise smooth 1-D signals but fail to do so for multi-dimensioned signals. Yet image multi-scale geometric analysis can extract the image's intrinsic geometrical structure efficiently, it ensures the representation of the most distinguished features of the remote sensing image. The Contourlet Transform (CT) is firstly introduced into region classification in this paper to extract the rotationally invariant features. Then, the Non-Subsampled Contourlet Transform (NSCT) is also introduced which can avoids pseudo-Gibbs phenomena around singularities during the pre-process of remote sensing image denoising, owing to the properties of shift-invariant. NSCT also enriches the set of basis functions that makes it possible to extract some critical signal features. The optimization of basis selection is proposed in the NSCT to ensure the decomposition based on the maximum information content.2. We consider the remote sensing image classification as a partitioning problem. The partition is composed of homogeneous regions, namely the classes, separated by regularized interfaces. A novel method based on geometric contour model using level set evolution for partitioning of aerial image is presented. We modify the classical Chan-Vese model to deal with the two classes partition, i.e. the man-made objects detection. And then, an improved multi-region classification model was proposed based on Chan-Vese's approach, which avoids the interactions between each level set function and speed up the curve evolution. By extending the improved models into vector image classification ones, these models could comprise the extracted features from image multi-scale geometric analysis, which will improve the classification result greatly. In order to avoid possible local minima in the level set evolution, we adjust the weighting coefficients of the multi-scale features in different evolution periods, instead of the classical technique which is only evolving in a multi-scale fashion.3. Some remote sensing images are so complicated that features in a certain class may be non-linearly distributed, and the traditional geometric contour models are only applicable to the linear feature partition problem. In order to achieve better classification results, the method of nonlinearly mapping extracted features to an easy classification space is presented in this paper. Consequentially, the sparse classifier is introduced to process these features, which is possible to classify the extracted features effectively. In our method, lots of training samples containing substantive information firstly yield the sparse classifier. Then pixels in the remote sensing image are labeled as different prediction values by the sparse classifier function. At last, the modified geometric contour model, which comprises the features of the prediction values, is built to deal with the non-linear situation. In the thesis, we also discuss each kind of sparse classifier method theoretically and demonstrate some fundamental experiments for comparison among them. According to the comparision results, the Kernel March Pursuit (KMP) approach is selected in our algorithm.