Estibaliz Martinez

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.

A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

In this letter, we propose a novel method for unsupervised change detection (CD) in multitemporal Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) satellite images by using the relative dimensionless global error in synthesis index locally. In order to obtain the change image, the index is calculated around a pixel neighborhood (3

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

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Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

3 window) processing simultaneously all the spectral bands available. With the objective of finding the binary change masks, six thresholding methods are selected. A comparison between the proposed method and the change vector analysis method is reported. The accuracy CD showed in the experimental results demonstrates the effectiveness of the proposed method.

Decision Management making by AHP (Analytical Hierarchy Process) trought GIS data

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.

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

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

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

In this work, we propose the use of the AHP (analytical hierarchy process) as a mathematical tool to structure a multiple criteria problem like a visual pattern. The main objective pursued is to determine what is the optimal placing to build a urban construction to locate a university library. In this case, it has been applied for the Campus of Montegancedo of the Polytechnic University of Madrid (Spain), where the Faculty of Computer science is placed. The data come from a Geographical Information System (GIS). Three different profiles of standard users have been considered and they determine the management of the final decision to take, in order to carry out the study. The use of the commercial software Expert Choice facilitates efficiently this management.

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

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.

A combined measure for quantifying and qualifying the topology preservation of 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.

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

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.