Satellite imagery fusion with an equalized trade-off between spectral and spatial quality

En este trabajo se propone una estrategia para obtener imágenes fusionadas con calidad espacial y espectral equilibradas. Esta estrategia está basada en una representación conjunta MultiDirección-MultiRresolución (MDMR), definida a partir de un banco de filtros direccional de paso bajo, complementada con una metodología de búsqueda orientada de los valores de los parámetros de diseño de este banco de filtros. La metodología de búsqueda es de carácter estocástico y optimiza una función objetivo asociada a la medida de la calidad espacial y espectral de la imagen fusionada. Los resultados obtenidos, muestran que un número pequeño de iteraciones del algoritmo de búsqueda propuesto, proporciona valores de los parámetros del banco de filtro que permiten obtener imágenes fusionadas con una calidad espectral superior a la de otros métodos investigados, manteniendo su calidad espacial

Passion for News Pamphlets: Bibliophilia, Bibliography and Documentation of cases

[Resumen] En este artículo, se dan a conocer varias de las joyas bibliográficas que formaron parte de la biblioteca particular de Juan Pérez de Guzmán y Boza, duque de T’Serclaes de Tilly, y otras procedentes de la colección de Henry Huth. Se trata de relaciones de sucesos muy curiosas, contenidas en pliegos sueltos, de las que también se ha intentado reconstruir su historia textual, tipográfica y editorial, con el fin de mostrar las distintas vías de producción, transmisión y difusión de las noticias que circularon por Europa durante la Edad Moderna[Abstract] This essay deals with a number of the rare and unusual printed news pamphlets that formed part of the private library of Juan Pérez de Guzmán y Boza, Duke of T'Serclaes de Tilly, as well as others that came from the collection of Henry Huth. The article seeks to reconstruct their textual, typographical and publishing history with a view to establishing the various different ways in which news stories were produced, transmitted and circulated across Europe in the early modern periodMinisterio de Ciencia e Innovación; FFI2009-08113Ministerio de Economía y Competitividad; FFI2012-3436

Adaptive Pointing Theory (APT) Artificial Neural Network

The choice value and the testing process against the vigilance parameter, characteristic of ART Neural Network, are merged. Only, a single unique test is required to determine if a committed category node can represent the current input or not. Advantages of APT over ART are: 1-Avoid testing every committed category node before deciding to train a committed category node or a new node must be committed, 2-The vigilance parameter is fixed during training, and 3-The choice value parameter is eliminated

Incidencia del tamaño de la ventana en la calidad de las imágenes fusionadas mediante mapas de dimensión fractal

El objetivo de este trabajo es investigar la influencia del tamaño de ventana (wsize) utilizado en un nuevo paradigma de fusión que pretende minimizar los efectos de alta variabilidad espacial y baja separabilidad espectral que caracterizan a las imágenes de alta resolución espacial obtenidas mediante algoritmos de fusión. Este paradigma de fusión se basa en mapas locales de dimensión fractal de las imágenes a fusionar. La obtención de estos mapas se ha llevado a cabo mediante un proceso de ventaneado y la utilización de un algoritmo particular para el cálculo de la dimensión fractal (box-counting). Este algoritmo implica la definición de un tamaño de ventana, wsize, el cual tiene una fuerte influencia en la estimación de la dimensión fractal local y consecuentemente en la calidad de las imágenes fusionadas. El estudios se ha llevado a cabo para un algoritmo de fusión basado en la Transformada Discreta Wavelet calculada mediante el algoritmo à trous

Aplicación de la Metodología de Fusión de Imágenes MDMR a la Estimación de la Turbidez en Lagos - Multidirection-Multiresolution Fusion Images Methology(MDMR) Applied to Turbidity Lake Estimation

Se propone mejorar la precisión en la estimación de características representativas de la calidad de las aguas de un lago mediante el uso de imágenes de satélite fusionadas. Las imágenes satelitales fuente han sido capturadas por los sensores a bordo del satélite Landsat 7. Las imágenes fusionadas se han obtenido mediante una nueva metodología de fusión, conceptualmente inspirada en una transformada multidirección-multirresolución (MDMR) y la transformada de ondículas calculada mediante el algoritmo de cavidades (Wavelet à trous), utilizando un banco de filtros direccionales y separables. La principal característica de esta metodología de fusión es el mecanismo de control de la calidad de las imágenes fusionadas. Los resultados muestran una notable mejora en la estimación de la calidad de las aguas del lago

Toward Multi-Scale Object-Based Data Fusion

This paper proposes a new methodology for object based 2-D data fu- sion, with a multiscale character. This methodology is intended to be use in agriculture, specifically in the characterization of the water status of different crops, so as to have an appropriate water management at a farm-holding scale. As a first approach to its evaluation, vegetation cover vigor data has been integrated with texture data. For this purpose, NDVI maps have been calculated using a multispectral image and Lacunarity maps from the panchromatic image. Preliminary results show this methodology is viable in the integration and management of large volumes of data, which characterize the behavior of agricultural covers at farm-holding scale

An Efficient Algorithm For Satellite Images Fusion Based On Contourlet Transform

This paper proposes a new fusion method for multiespectral (MULTI) and panchromatic (PAN) images that uses a highly anisotropic and redundant representation of images. This methodology join the simplicity of the Wavelet transform, calculated using the à trous algorithm, with the benefits of multidirectional transforms like Contourlet Transform. That has permitted an adequate extraction of information from the source images, in order to obtain fused images with high spatial and spectral quality simultaneously. The new method has been implemented through a directional low pass filter bank with low computational complexity. The source images correspond to those captured by the IKONOS satellite (panchromatic and multispectral). The influence of the filter bank parameters in the global quality of the fused images has been investigated. The results obtained indicate that the proposed methodology provides an objective control of the spatial and spectral quality trade-off of the fused images by the determination of an appropriate set of filter bank parameters

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information

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

The Self-OrganizingMap (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 topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving ma