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

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

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

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

Integration of Panchromatic and Multispectral Images by Local Fractal Dimension

The fusion image strategies are a good solution to obtain a synthetic image with high spatial and spectral characteristics simultaneously. Some of them are based on the Wavelet Transform, computed by means of the à trous algorithm (AWT). Most of them do not differentiated between spectral bands. In this sense, a new approach that weights differently the spatial information integrated from the high resolution image in each of the fused image spectral bands by the optimization of the trade off between the spatial and spectral quality of the fused images, was proposed. The main problems of this approach are that a unique weighting factor for the whole spectral band is computed, and the need of indices, that separately measure the spectral and spatial quality of the fused images. In this work, a new strategy that tries to avoid the problems above mentioned is introduced. For that, it is proposed to determine a local weighting factor for each panchromatic pixel by means the fractal map, using the box-counting algorithm. Panchromatic and multispectral Quickbird images have been used to show the performances of this new methodology. The local quality of the final fused images has been evaluated by means of local quality maps of Q index. It has been proved that the proposed fusion strategy preserve the high frequency information of the panchromatic image in areas with a high detail, while in homogeneous areas the low frequency information of the multispectral image are conserved

Caracterización multiescala de objetos como herramienta para la clasificación de imágenes de alta resolución espacial

This paper presents a new methodology, simple and affordable, for the definition and characterization of objects at different scales in high spatial resolution images. The objects have been generated by integrating texturally and spectrally homogeneous segments. The former have been obtained from the segmentation of Wavelet coefficients of the panchromatic image. The multi-scale character of this transform has yielded texturally homogeneous segments of different sizes for each of the scales. The spectrally homogeneous segments have been obtained by segmenting the classified corresponding multispectral image. In this way, it has been defined a set of objects characterized by different attributes, which give to the objects a semantic meaning, allowing to determine the similarities and differences between them. To demonstrate the capabilities of the methodology proposed, different experiments of unsupervised classification of a Quickbird image have been carried out, using different subsets of attributes and 1-D ascendant hierarchical classifier. Obtained results have shown the capability of the proposed methodology for separating semantic objects at different scales, as well as, its advantages against pixel-based image interpretation

Influence of source images spatial characteristics on the global quality of fused images

techniques to perform remote sensed image fusion are based on multiresolution analysis. This kind of images analysis requires the decomposition of the image at differente scales or levels, depending the fusion results on this level. Then, the two main objectives of this work are: to investigate the influence of the source images spatial characteristics on the decomposition level that the process fusion should be performed in; and to show how depends the spatial-spectral quality of fused images on this decomposition level. To carry out this study, the image fusion methodology that has been applied is based on the Wavelet transform, calculated by the à trous algorithm. The quality of the fused images has been evaluated by the ERGAS indices, as well as, the spectral correlation, the spatial correlation (Zhou’s index) and a global index (Q4). This methodology has been applied to fuse several multispectral and panchromatic images registered by the corresponding sensors on board the Landsat, Ikonos, and Quickbird satellites. It has been demonstrated that, in the majority of the cases, a low number of decompositions provides fused images with a high spatial and spectral quality trade-off. Additionally, the results indicate that the decomposition level that provides the best spatial-spectral quality trade-off depends on the spatial frequencies content of the source images