The aim of this study is to determine in aerial images the most significant parameters to automatic recognition of objects or textures such as house roof, vegetation, Ã Statistical method is Principal Component Analysis, a technique for classification and for examining relationships among quantitative variables. For Image Processing, Java Language is used. For statistics, SAS programs (Statistical Analysis System) with IML package (Interactive Matrix Language). Aerial Images are 400*400 pixels with 10 pixels for 1 meter on ground, with the three r,g,b components (red,green,blue). With image a first simple example and fast operator is performed to find contours, then algorithm has been adapted to dene regions with growing and merging of zones. On those zones a set of parameters is calculated to perform Principal Component Analysis for summarizing data. Results of this method allow classification of zones and find the most significant parameters to be selected, levels of r,g,b components, dispersion of those components, and one calculated value in relation with geometric characteristics of each zone.
In this paper, we summarize and compare two different approaches used by the authors, to classify different natural textures. The first approach, which is simple and inexpensive in computing time, uses a data bank image and an expert system able to classify different textures from a number of rules established by discipline specialists. The second method uses the same database and a neural networks approach.
The tools developed by the School of geostatistical have many applications for image segmentation . First, it is very suited to the analysis of natural images eg from remote sensing images and medical images. secondly, they are less expensive in time calculation, as can the methods, from Fourier analysis or matrices coocurrences. We offer reviews of various works of authors to segment natural textures.
The purpose of this paper is the study of efficient methods for image binarization. The objective of the work is the metro maps binarization. the goal is to binarize, avoiding noise to disturb the reading of subway stations. Different methods have been tested. By this way, a method given by Otsu gives particularly interesting results. The difficulty of the binarization is the choice of this threshold in order to reconstruct. Image sticky as possible to reality. Vectorization is a step subsequent to that of the binarization. It is to retrieve the coordinates points containing information and to store them in the two matrices X and Y. Subsequently, these matrices can be exported to a file format 'CSV' (Comma Separated Value) enabling us to deal with them in a variety of software including Excel. The algorithm uses quite a time calculation in Matlab because it is composed of two "for" loops nested. But the "for" loops are poorly supported by Matlab, especially in each other. This therefore penalizes the computation time, but seems the only method to do this.
In this paper we propose to simulate SAR radar images that can be acquired by aircraft or satellite. This corresponds to a real problematic, in fact, an airborne radar data acquisition campaign, was conducted in the south east of France. We want to estimate the geometric deformations that a digital terrain model can be subjected. By extrapolation, this construction should also allow to understand the image distortion if a plane is replaced by a satellite. This manipulation allow to judge the relevance of a space mission to quantify geological and geomorphological data. The radar wave is an electromagnetic wave, they have the advantage of overcoming atmospheric conditions since more wavelength is large is better crossing the cloud layer. Therefore imaging radar provides continuous monitoring.
Analysis and automatic segmentation of texture is always a delicate problem. Objectively, one can opt, quite naturally, for a statistical approach. Based on higher moments, these technics are very reliable and accurate but expensive experimentally. We propose in this paper, a well-proven approach for texture analysis in remote sensing, based on geostatistics. The labeling of different textures like ice, clouds, water and forest on a sample test image is learned by a neural network. The texture parameters are extracted from the shape of the autocorrelation function, calculated on the appropriate window sizes for the optimal characterization of textures. A mathematical model from fractal geometry is particularly well suited to characterize the cloud texture. It provides a very fine segmentation between the texture and the cloud from the ice. The geostatistical parameters are entered as a vector characterize by textures. A neural network and a robust multilayer are then asked to rank all the images in the database from a learning set correctly selected. In the design phase, several alternatives were considered and it turns out that a network with three layers is very suitable for the proposed classification. Therefore it contains a layer of input neurons, an intermediate layer and a layer of output. With the coming of the learning phase the results of the classifications are very good. This approach can bring precious geographic information system. such as the exploitation of the cloud texture (or disposal) if we want to focus on other thematic deforestation, changes in the ice ...