Atef Hamouda

New approach for road extraction from high resolution remotely sensed images using the quaternionic wavelet

Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work and therefore are very expensive and time consuming. This paper presents an efficient and computationally fast method to extract road from very high resolution images automatically. We propose in this paper a new approach for following roads path based on a quaternionic wavelet transform insuring a good local space-frequency analysis with very important directional selectivity. In fact, the rich phase information given by this hypercomplex transform overcomes the lack of shift invariance property shown by the real discrete wavelet transform and the poor directional selectivity of both real and complex wavelet transform.

Fast communication: Generalized multi-directional discrete Radon transform

This paper presents a discrete generalized multi-directional Radon transform (GMDRT) and its exact inversion algorithm. GMDRT is an extension of the classical Radon transform. It aims to project parameterized curves and geometric objects following several directions. For this purpose, we propose an algebraic formalism of the Radon Transform presenting the forward transform as a matrix-vector_ multiplication. We show in this paper that the exact inversion of the GMDRT exists. This property allows useful applications, in the field of digital image processing.

Template-Based Hierarchical Building Extraction

Automatic building extraction is an important field of research in remote sensing. This letter introduces a new object-based building extraction approach. So far, many object-based algorithms for building extraction have been proposed. However, these algorithms mainly operate in two phases: object construction and building extraction. The majority of these algorithms heavily relies on the object construction process, mainly due to the lack of interaction between the two steps. To overcome these drawbacks, we introduce a new hierarchical approach based on building templates. Carried out experiments on data sets of images from the urban area of Strasbourg show the benefits of our approach.

Hierarchical classification-based region growing (HCBRG): a collaborative approach for object segmentation and classification

Object-based image classification approaches heavily rely on the segmentation process. However, the lack of interaction between both segmentation and classification steps is one of the major limits of these approaches. In this paper, we introduce a hierarchical classification based on a region growing approach driven by expert knowledge represented in a concept hierarchy. In order to overcome the region growings limits, a first classification will associate a confidence score to each region in the image. This score will be used through an iterative step, which allows interaction between segmentation and classification at each iteration. Carried out experiments on a Quickbird image show the benefits of the introduced approach.

Kohonen’s Map Approach for the Belief Mass Modeling

In the framework of the evidence theory, several approaches for estimating belief functions are proposed. However, they generally suffer from the problem of masses attribution in the case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using Kohonens map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision, and paradox. The proposed method for a basic belief assignment (BBA) is of interest for solving estimation mass functions problems where a large quantity of multivariate data is available. Indeed, the use of Kohonens map simplifies the process of assigning mass functions. The proposed method has been compared with the state-of-the-art BBA technique on benchmark database and applied on remote sensing data for image classification purpose. Experimentation shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle a large amount of data.

Rθ-signature: A new signature based on Radon Transform and its application in buildings extraction

Object recognition has been a topic of research for decades, it operates by making decisions based on the values of several shape properties measured from an image of the object. In this paper, a new exploitation of the Radon Transform (RT) is proposed to extract only one projection according to a single angle. This projection is chosen in way that contains the necessary information to recognize an object (a shape descriptor). This descriptor (called Rθ-signature) provides global information of a binary shape regardless its form. After that, we use this signature in an extraction method of buildings from very high-resolution satellite imagery.

Line extraction algorithm based on image vectorization

The raster-vector conversion of remote sensing image is a very important task in the extraction and updating of linear objects in cartographic processes. In this paper we present a vectorization method, based on constrained Delaunay triangulation, for line extraction. The constraints are provided by a preprocessing step insuring that these edges belong to line structures in the original image. The vectorization is performed using CDT and resulting triangles are grouped into polygins that make up the vector image. The skeleton of these polygons represents the extracted linear structures. The algorithm is automatic, fast and has very satisfing results when tested on road segments.

Urban road extraction from high-resolution optical satellite images

Road extraction research has always been an active research on automatic identification of remote sensing images. With the availability of high spatial resolution images from new generation commercial sensors, how to extract roads quickly, accurately and automatically has been a cutting-edge problem in remote sensing related fields. In this paper, we present a novel road extraction approach which uses a scale space segmentation and two measures of the shape index to filter all regions from the result of the segmentation. The approach makes full use of spectral and geometric properties of roads in the imagery, and proposes a new algorithm named “Road Segments joint Algorithm” to ensure the continuity of roads.

The Kohonen map for credal classification of large multispectral images

In the framework of the evidence theory, several approaches for estimating belief functions have been proposed. However, they generally suffer from the problem of masses attribution in case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using the Kohonen map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision and paradox. The proposed method for basic belief assignment (BBA) is of interested for solving estimation mass functions problems where a large quantities of multi-variate data is available, such as remote sensing images. Indeed, the use of the Kohonen map simplify the process of assigning mass functions. In order to experimentally validate our work, we applied the proposed method to real data sets on image classification problem. Experimentation on SPOT images show that our approach gives better results than other methods presented in the literature.

On the Estimation of Mass Functions Using Self Organizing Maps

In this paper, an innovative method for estimating mass functions using Kohonens Self Organizing Map is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. This new method is of interest for solving estimation mass functions problems where a large quantity of multi-variate data is available. Indeed, the use of Kohonen map that allows to approximate the feature space dimension into a projected 2D space (so called map) simplifies the process of assigning mass functions. Experimentation on a benchmark database shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle large amount of data.