Decision fusion for reliable flood mapping using remote sensing images

Flood extent mapping is a basic tool for flood damage assessment, which can be done by digital classification techniques using satellite imageries, including the data recorded by radar and optical sensors. However, converting the data into the information we need is not a straightforward task. One of the great challenges involved in the data interpretation is to separate the permanent water bodies and flooding regions, including both the fully inundated areas and the wet areas where trees and houses are partly covered with water. This paper adopts the decision fusion technique to combine the mapping results from radar data and the NDVI data derived from optical data. An improved capacity in terms of identifying the permanent or semi-permanent water bodies from flood inundated areas has been achieved. Computer software tools Multispec and Matlab were used.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Mixed pixel analysis and assessment for flood mapping

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

Aportaciones metodológicas a la gestión y explotación de nubes de puntos procedentes de escáneres tridimensionales, aplicados a la documentación geométrica del patrimonio. El caso de la Portada de los Hierros de la Catedral de Valencia

Presentado en: IX Congreso Internacional de Rehabilitación del Patrimonio Arquitectónico y Edificación (Sevilla, España, 9-12 julio 2008)

Methodological keys for the acquisition and long-term use of photographic collections representing elements of Heritage

Contributed to: Fusion of Cultures. XXXVIII Annual Conference on Computer Applications and Quantitative Methods in Archaeology – CAA2010 (Granada, Spain, Apr 6-9, 2010)

[A_Laguardia_SanJuan] Levantamiento fotogramétrico de la portada Sur o “de los abuelos” de la iglesia de San Juan de Laguardia (Álava)

[ES] La documentación contenida en este registro ha servido de base para los siguientes documentos:

[R_Clavijo_SanPrudencio] Intensive Program ERASMUS: TOPCART. Geometric Documentation of the Heritage (report of activities 2010-2011)

[EN] Data contained in this record come from the following accademic activity (from which it is possible to locate additional records related with the Monastery):

[A_Elvillar_Hechicera] Documentación geométrica del dolmen de la Chabola de la Hechicera (Elvillar, Álava)

[ES] Tumba megalítica compuesta por 17 losas de gran tamaño, incluida la tapa. La estructura ocupa un espacio de 10 x 4 metros en planta, unos 4 metros de altura en la cámara. Conserva restos del túmulo que forma aproximadamente un círculo de unos 10 metros de radio.

Documentación y representación de paramentos verticales en riesgo de colapso. Aplicación en el Monasterio de San Prudencio (Clavijo, La Rioja)

[ES] Los datos de este registro provienen de la una actividad académica que también aparece descrita en el repositorio y desde donde se puede acceder a otros trabajos relacionados con el Monasterio:

Metodología fotogramétrica para la cuantificación de las alteraciones en paramentos pétreos patrimoniales. Aplicación a la iglesia de San Juan de Laguardia

[ES] Este proyecto fin de carrera está realacionado con el siguiente proyecto de documentación de un elemento patrimonial:

Geometric documentation and reconstruction of the church of the Monastery of San Prudencio (La Rioja, Spain)

[EN] Data contained in this record come from the following accademic activity (from which it is possible to locate additional records related with the Monastery):

[A_Vitoria_SantaMaria] Documentación geométrica de las excavaciones de la Catedral de Santa María (Vitoria-Gasteiz). Mayo 2000-Junio 2003

[ES] Información sobre este proyecto ha servido de base a los siguientes artículos:

[R_SanMillan_Yuso] Documentación geométrica de las excavaciones arqueológicas realizadas en el Monasterio de Yuso (San Millán de la Cogolla, La Rioja)

[ES] El seguimiento arqueológico se realiza sobre las capillas del la nave Norte de la iglesia más la capilla “de las reliquias”, en total 9 capillas de unos 10 x 10 metros cada una. Los elementos a documentar son las unidades estratigráficas exhumadas que, en gran medida corresponden a enterramientos (fosas y los propios esqueletos).

[G_Oñati_Arantzazu] Documentación fotográfica del Apostolado y la Piedad del exterior del Monasterio de Arantzazu (Oñati, Gipuzkoa)

[ES] Conjunto de figuras en piedra de gran porte (unos 4 metros de altura cada una), lo componen 14 figuras en la zona baja del apostolado y un conjunto de 2 personajes más formando la Piedad en la parte superior. Se trata de formas esquemáticas de perfil redondeado que corresponden artísticamente a una de las corrientes más destacadas del siglo XX.