Mohamed Rached Boussema

The contribution of the sources separation method in the decomposition of mixed pixels

In this paper, we propose to prove the importance of the application of blind sources separation methods on remote sensing data. Indeed, satellite images are represented by radiometric values where each one is considered as a mixture of different sources. The primary goal of our research is to hand back the different sources covering the scanned zone. The main constraint to restore these sources is to take our observation images as a mixture of physically independent components. In our work, the independence between the different sources is obtained through two statistical methods. The first method is based on the reduction of the spatial source correlations, and the second one is based on the joint maximization of the fourth-order cumulants. On the opposite of the original multispectral images that are represented according to correlated axes, the source images extracted from the proposed algorithms are represented according to mutually independent axes that allow each source to represent specifically a certain type of land cover. This increases the reliability of the analysis and the interpretation of the scanned zone. The source images obtained from the application of the sources separation method give a more effective representation of the information contained on the observation images. The performance of these source images is investigated through an application for the decomposition of mixed pixels. The originality of our application comes from the determination of the mixing matrix modeling the spectral endmembers based on source filters. These filters model the sensibility of each source channel according to the different spectral bands, which give an interesting information about the spectral theme represented by the corresponding source image. This application shows that the proportions of the different land cover types existing into the pixel are better estimated through the source images than through the original multispectral images. This method could offer an interesting solution to mixed-pixel classification.

A comparative study for unmixing based Landsat ETM+ and ASTER image fusion

Abstract The mineral environment of the Bouaouane–Jebel (Hill) Hallouf mine, in the north of Tunisia, is monitored and analyzed by making use of the laboratory analysis and remote sensing images, ETM+ as well as ASTER VNIR and SWIR data, acquired in the same period. The main contribution of this paper consists of a methodology using multispectral multi-sensor fusion for the refinement of the mine tailing cartography around the studied mine. The developed methodology is based on the linear spectral unmixing approach which is applied to a multispectral hybrid image. This image was generated from the fusion of Landsat ETM+ and ASTER SWIR data. A comparative study is made between the hybrid and ASTER (VNIR and SWIR) images classification with respect to laboratory analysis. The given results show that the fusion of Landsat ETM+ and ASTER SWIR multispectral image yields the best mineral detection.

A Spectral and Spatial Source Separation of Multispectral Images

This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earths surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained sources represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images

A New Sparse Source Separation-Based Classification Approach

In many geoscience applications, we have to convert remotely sensed images to ground cover maps. Numerous approaches to extract ground cover information have been developed. Recently, blind source separation (BSS) of remote-sensing data has received significant attention due to its suitability to recover sources when no information is available about the scanned zone, hence the term blind. In the remote-sensing context, associating each source to a significant land cover theme is difficult and constitutes the real challenge of this paper. Many authors have pointed out that BSS is overwhelmingly a question of contrast and diversity. This reasoning motivates this work which takes advantage of both decorrelation and sparsity to propose a two-level novel approach to separate our different land covers called sources. The first separation stage is based on second-order statistics or decorrelation. It gives a suitable representation of the remote-sensing images. However, decorrelation is a natural way of differentiating statistically between sources but is unable to identify and extract finer features with physical meaning. The aim of the second separation stage is to overcome this problem by an increasingly popular and powerful assumption which is the sparse representation. The last leads to good separation because most of the energy in the defined basis, at any time instant, belongs to a single source. This allows the extraction of physical features and the capture of image essential structures. The innovative aspect of this study concerns the development of a new image classification approach that integrates the BSS at the feature extraction level to provide the most relevant sources from remotely sensed images. It can be viewed as an unsupervised classification method. The second-order separation process is used as a preprocessing step to remove the interband correlation which sometimes brings ill effect to image classification. However, the second-order process is unable to uncover the underlying sources. The basic idea behind our approach is that heterogeneous multichannel data provide sparse spectral signatures in addition to sparse spatial morphologies in specified dictionaries. Hence, sparse modeling can be used to disentangle the land covers from observed mixtures. From the sparse representation, the data space is transformed into a feature space composed of mutually exclusive classes. Finally, we will merge these classes at the decision level in order to enhance the semantic capability and the reliability of land cover classification. The effectiveness of the proposed approach was demonstrated by operating two experiments to study respectively the source separation and the image classification capability of the developed approach. The different results on remote-sensing images illustrate the good performance of the new sparse approach and its robustness to noise. These experiments show that the sparse representation enhances the separation quality and allows extracting more easily the essential structures of the scanned zone. The proposed approach offers an interesting solution to the classification process with limited knowledge of ground truth.

Unmixing based Landsat ETM+ and ASTER image fusion for hybrid multispectral image analysis

The mine of Bouaouane-Djbel (Hill) Hallouf which is exploited for the lead and zinc ores is among several types of mines in the Medjerda river watershed. We propose a multispectra inter-images fusion using a simplified version of multisensor multiresolution technique (MMT) for mine tailing cartography refinement. We use Landsat MS/Pan fused image and ASTER SWIR image acquired in the same period to conserve mineral state. Classification of the resulting Hybrid multispectral image based on constrained and unconstrained linear spectral unmixing is performing using end member library spectra. Unmixing results coincide with ASTER TIR interpretation as well as laboratory analysis. Moreover, the given results show that Hybrid multispectral image is more precise for certain mineral detection than ASTER fused image.

Mixed pixel decomposition of satellite images based on source separation method

In this paper we propose to prove the importance of the application of blind source separation methods on remote sensing data. Satellite images are represented by radiometric value that can be considered as a mixture of independent sources. To restore the independent sources we use the statistical method of Joint Approximate Diagonalization of Eigen-matrix (JADE). The proposed algorithm generates source images where each one gives a maximum of information specific to a certain type of land cover. These source images do not provide one scalar value per pixel, but rather a vector which components will agree with the radiometric value of the different land cover types present in the pixel.

Perceptron nonlinear blind source separation for feature extraction and image classification

In this paper, we aim to classify remotely sensed images for land characterisation. The major goal is approaching the natural nonlinear mixture for band observation and then dimension reduction by supervised classification. After that, an unsupervised method combining feature extraction and SVM in investigating to discriminate the land cover for SPOT 4 satellite image. In this technique, training data base are wavelet features that are extracted from a subset of sources.

Tailing modelled and measured spectrum for mine tailing mapping in tunisian semi-arid context

Mine tailings may have a widespread geographical distribution; their location and extent may also vary along time, due to reprocessing and disposal activities. Remote sensing techniques have been proven extremely valuable in the inventory, characterization, and remediation of mine tailings elsewhere. In this study we focus on mine tailing mapping around Jebel Hallouf-Bouaouane mine using Landsat multispectral data. Field spectral data, measured on the ground by a spectroradiometer are very important and needed for mine tailing classification. These informations, more faithful to the natural conditions, are unavailable in some dates. Our methodology is based on a Spectral Modelling Approach SMA using the JPL reflectance library. The main contribution is to evaluate the tailing modelled reflectance proposed to replace the lack of the field data needed for mine tailing mapping. Linear spectral unmixing is applied for mine tailing map generation using the tailing modelled spectrum, the ASD measured spectrum and the ETM+ derived spectrum. The comparison of results indicate that the SMA approach can be applied successfully to multispectral data analysis, particularly those acquired during previous periods. The SMA can be an optimal solution to replace the lack of measured field data (by the spectroradiometer).

Retrieval of multi-scale roughness parameters and soil moisture by numerical inversion

The aim of this present work is to find an inverse model to retrieve roughness geometric and dielectric parameters of natural rough surfaces from radar backscattering data. The bi-dimensional surfaces are described by means of the fractional Brownian motion random process, using the bi-dimensional wavelet transform. Multi-scale roughness is characterized by two parameters, the first one proportional to the standard deviation and the other one related to the fractal dimension. Soil moisture is related to the complex dielectric constant. To simulate radar backscattering we used the small perturbation model in which the radar backscattering coefficient can be expressed as the product of two factors, the first dependent on polarisation but independent of surface roughness and vice versa the second dependent on roughness but independent of polarisation. Thus, this model simplifies the procedure of inversion and the co-polarised ratio between hh and vv polarisation is independent of roughness and a minimisation over only two parameters is performed to retrieve the complex dielectric constant independently of the employed geometric surface description. Once the dielectric constant is known, the retrieval of multi-scale surface roughness parameters is performed in a successive step by using multi-frequency and multi-incident angle data.

A study of radar backscattering on multi-scale bi-dimensional rough surfaces

The principal objective of the present work is to find an adequate description of natural rough surfaces in order to study backscattering from these surfaces and try in a future work to retrieve soil characteristic parameters. Previous work has shown the inadequacy of the classical description which considered rough surfaces as stationary random Gaussian processes. They proposed a multi-scale surface description considering one-dimensional surfaces and incorporated it into the integral equation model (IEM). We extend this multi-scale description to the bi-dimensional case using the Mallat algorithm and we investigate its impact on radar backscattering.