Consuelo Gonzalo

Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain

A new methodology for fusing satellite sensor imagery, based on tailored filtering in the Fourier domain is proposed. Finite‐duration Impulse Response (FIR) filters have been designed through an objective criterion, which depends on source image characteristics only. The designed filters allow a weighted fusion of the information contained in a fine spatial resolution image (PAN) and in a multispectral image (MULTI), respectively, establishing a trade‐off between spatial and spectral quality of the resulting fused image. This new technique has been tested with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Spatial and spectral quality of the fused images was compared with the results provided by Mallats Wavelet algorithm. The images fused by the proposed method were characterized by a spatial resolution very close to the PAN image, and by the spectral resolution of the MULTI image.

Spectral or spatial quality for fused satellite imagery? A trade‐off solution using the wavelet à trous algorithm

Several different methods for the fusion of multispectral and panchromatic images based on the wavelet transform have been proposed. The majority provide satisfactory results, but there is one, the à trous algorithm, that presents several advantages over the other fusion methods. Its computation is very simple; it only involves elementary algebraic operations, such as products, differences and convolutions. It yields a better spatial and spectral quality than the other methods. Standard fusion methods do not allow control of the spatial and spectral quality of the fused image; high spectral quality implies low spatial quality and vice versa. This paper proposes a new version of a fusion method based on the wavelet transform, computed through the à trous algorithm, that permits customization of the trade‐off between the spectral and spatial quality of the fused image through the evaluation of two quality indices: a spectral index (the ERGAS index) and a spatial one. For the latter, a new spatial index based on ERGAS concepts and translated to the spatial domain has been defined. In addition, several different schemes for the computation of the fusion method investigated have been evaluated to optimize the degradation level of the source image required to perform the fusion process. The performance of the proposed fusion method has been compared with the fusion methods based on wavelet Mallat and filtering in the Fourier domain.

Toward reduction of artifacts in fused images

Abstract Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e. artifacts, in the resulting fused images. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the a trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fused images and their classification results when compared with the original WAT method.

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earths surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process.

A directed search algorithm for setting the spectral-spatial quality trade-off of fused images by the wavelet à trous method

This paper proposes a method to determine, in an objective and accurate way, the weighting factor (α) to be applied to the detailed panchromatic image information that will be integrated with the background multispectral image information to obtain the “best” fused image with the same spatial and spectral quality. The fusion method is a weighting variant of the fusion algorithm based on the wavelet transform, calculated using the à trous (WAT) algorithm. The α factor is determined, for each band of the multispectral source images using the simulated annealing (SA) search algorithm, which optimizes an objective function (OF) associated with both spatial and spectral quality measures for the fused images. The results obtained have demonstrated that for each one of the spectral bands there is an α value that provides fused images with the optimal trade-off between the two qualities for any decomposition level value (n) of the wavelet transform.

Improvement of self-organizing maps with growing capability for goodness evaluation of multispectral training patterns

In this paper, self-organizing maps (SOM) with growing capability are proposed to evaluate the goodness of multispectral training areas selection that would be used in supervised classification processes. The SOM model used in this study is the Growing Cell Structures (GCS) neural network. Some modifications of the original GCS training algorithm are proposed in order to make easy the physical interpretation of their parameters. In addition, several visualization methods have been implemented with the aim of displaying the trained GCS networks. The performances of the modified GCS model have been investigated through a large number of experiments. They have been carried out using multispectral data registered by ETM+ sensor (Landsat 7), to discriminate land cover categories. The results confirm the excellent behavior of the GCS modified training algorithm to evaluate the quality of the selected training patterns, their viability for feeding supervised classification models and their refining.

Visualizing high-dimensional input data with growing self-organizing maps

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen self-organizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.

Evaluation of different fuzzy knowledge acquisition methods for remote sensing image classification

Three fuzzy knowledge acquisition methods have been implemented and compared. Methods comparison has carried out through the evaluation of their classification performances. Using minimum spatial and spectral information and reducing as much as possible the rules number has done the study.

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

Abstract The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, particularly using Kohonens model. In this work, two methods for measuring the topology preservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map.

Aplicación de la Metodología de Fusión de Imágenes Multidirección-Multiresolución (MDMR) a la Estimación de la Turbidez en Lagos

It is proposed to improve the precision estimation of the representative characteristics of the quality characteristics of waters of a lake through the use of fused images. For that, images captured by the sensors of the Landsat 7 satellite have been used. The fused images have been obtained by means of a new fusion methodology, conceptually inspired by the Multidirection-Multiresolution Fusion Images Methology (MDMR) and by the Wavelet a trous algorithm, using a directional and separable filter bank. The main characteristic of this approach is the fused images quality control mechanism. The results show a noticeable improving in the reliability of the lake water quality estimation.