Papers by Consuelo Gonzalo
2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)
Global change is one of the outstanding problems nowadays. This is the reason why considerable at... more Global change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.
International journal of geoinformatics, 2008
This paper proposes a new fusion method for multiespectral (MULTI) and panchromatic (PAN) images ... more 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 a 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 image...
La delimitacion de parcelas agricolas a gran escala juega un papel primordial en los estudios rel... more La delimitacion de parcelas agricolas a gran escala juega un papel primordial en los estudios relacionados con el manejo y la gestion de las explotaciones agricolas. Por ello, es necesario el diseno de metodos computacionales que realicen esta labor de forma automatica, rapida y precisa. El objetivo de este trabajo es proporcionar un marco que permita la correcta delimitacion de las parcelas. La metodologia propuesta emplea un consenso de segmentaciones a diferentes escalas para proporcionar informacion acerca de los bordes mas significativos correspondientes a las parcelas agricolas presentes en una imagen de muy alta resolucion espacial.
GEOBIA 2016 : Solutions and Synergies, 2016
Recently deep learning-based methods have demonstrated excellent performance on different artific... more Recently deep learning-based methods have demonstrated excellent performance on different artificial-intelligence tasks. Even though, in the last years, several related works are found in the literature in the remote sensing field, a small percentage of them address the classification problem. These works propose schemes based on image patches to perform pixel-based image classification. Due to the typical remote sensing image size, the main drawback of these schemes is the time required by the window-sliding process implied in them. In this work, we propose a strategy to reduce the time spent on the classification of a new image through the use of superpixel segmentation. Several experiments using CNNs trained with different sizes of patches and superpixels have been performed on the ISPRS semantic labeling benchmark. Obtained results show that while the accuracy of the classification carried out by using superpixels is similar to the results generated by pixel-based approach, the expended time is dramatically decreased by means of reducing the number of elements to label.
GEOBIA 2016 : Solutions and Synergies, 2016
The generation of labeled data for training automated methods used in the analysis of remote sens... more The generation of labeled data for training automated methods used in the analysis of remote sensing images is a challenging task. Approaches as Active Learning aim to perform accurate classifications in a scenario of a few annotated data. These approaches generally require the interaction between the user and the machine learning method during training phase. However, in the remote sensing area, it is difficult to find a tool that facilitates this interaction. In this work, an interactive web-based platform to perform the training of method for remote sensing image annotation by means of an active learning approach is proposed. The platform integrates open-source GIS technologies and object-based approach in order to facilitate the interaction between the user and the active learning approach. While some usability aspects should be improved, the obtained results show the potential of the proposed platform.
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; th... more Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; therefore, the need of environmental knowledge is important to develop the appropriate management plans. In this context, remote sensing and, specifically, hyperspectral imagery (HSI) can contribute to the generation of vegetation maps for ecosystem monitoring. To properly obtain such information and to address the mixed pixels inconvenience, the richness of the hyperspectral data allows the application of unmixing techniques. In this sense, a problem found by the traditional linear mixing model (LMM), a fully constrained least squared unmixing (FCLSU), is the lack of ability to account for spectral variability. This paper focuses on assessing the performance of different spectral unmixing models depending on the quality and quantity of endmembers. A complex mountainous ecosystem with high spectral changes was selected. Specifically, FCLSU and 3 approaches, which consider the spectral variability, were studied: scaled constrained least squares unmixing (SCLSU), Extended LMM (ELMM) and Robust ELMM (RELMM). The analysis includes two study cases: 1) robust endmembers and 2) nonrobust endmembers. Performances were computed using the reconstructed root-mean-square error (RMSE) and classification maps taking the abundances maps as inputs. It was demonstrated that advanced unmixing techniques are needed to address the spectral variability to get accurate abundances estimations. RELMM obtained excellent RMSE values and accurate classification maps with very little knowledge of the scene and minimum effort in the selection of
Entropy
Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regi... more Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.
Remote Sensing
The outlining of agricultural land is an important task for obtaining primary information used to... more The outlining of agricultural land is an important task for obtaining primary information used to create agricultural policies, estimate subsidies and agricultural insurance, and update agricultural geographical databases, among others. Most of the automatic and semi-automatic methods used for outlining agricultural plots using remotely sensed imagery are based on image segmentation. However, these approaches are usually sensitive to intra-plot variability and depend on the selection of the correct parameters, resulting in a poor performance due to the variability in the shape, size, and texture of the agricultural landscapes. In this work, a new methodology based on consensus image segmentation for outlining agricultural plots is presented. The proposed methodology combines segmentation at different scales—carried out using a superpixel (SP) method—and different dates from the same growing season to obtain a single segmentation of the agricultural plots. A visual and numerical comp...
International journal for numerical methods in biomedical engineering, Jan 27, 2017
New image fusion rules for multimodal medical images are proposed in this work. Image fusion rule... more New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, random forest is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with 4 existing measures has been presented, which helps to compare the performance of 2 pixel level fusion methods. The experimental results clearly indica...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Studies in health technology and informatics, 2017
Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated cur... more Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approaches to process the unstructured texts in EHRs are based in applying text mining or natural language processing (NLP) techniques over the data. In particular Named Entity Recognition (NER) is of paramount importance to retrieve specific biomedical concepts from the text providing the semantic type of the concept retrieved. However, it is very common that clinical notes contain lots of acronyms that cannot be identified by NER processes and even if they are identified, an acronym may correspond to several meanings, so disambiguation of the found term is needed. In this work we provide an approach to perform acronym disambiguation in Spanish EHR using machine learning techniques.
Sensors, 2017
Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resou... more Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources' reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet 'à trous' through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet 'à trous' through fractal dimension maps as the best fusion algorithm for this ecosystem.
Journal of Maps, 2016
Water resources are the main productive source in Chile. Growing competition for water use causes... more Water resources are the main productive source in Chile. Growing competition for water use causes conflicts which end up in courts (Courts of Appeal and The Supreme Court). Legal disputes end when the courts issue a judgement which is recorded. The volume of court decisions made makes the task of searching, analyzing, and extracting knowledge from these texts challenging. This research is aimed at developing a mapping tool to explore spatiotemporal patterns from legal records, as court decisions are an objective proxy for conflicts. Natural Language Processing techniques are applied in order to process and extract information from the court decisions to help the map visualization process. The mapping tool allows information to be visualized in different layers ranging from the whole text of the courts' decisions to maps in which one can easily find the location of the conflict or any other places mentioned in the courts' decisions being analyzed by the tool by filtering the courts' decisions through different terms and concepts. To implement the mapping tools, we combined a geographic information system, along with a search engine (Elasticsearch) and an analytic dashboard (Kibana).
International Journal of Pattern Recognition and Artificial Intelligence, 2016
A new image fusion algorithm based on the visible and thermal images for face recognition is pres... more A new image fusion algorithm based on the visible and thermal images for face recognition is presented in this paper. The new fusion algorithm derives the benefit from both the modalities images. The proposed fusion process is the weighted sum of thermal and visible face information with two weighting factors [Formula: see text] and [Formula: see text], respectively. The weighting factors are calculated using a directed search algorithm automatically. The proposed fusion framework is evaluated through extensive experiments using UGC-JU face database. Experiments are of three fold. Firstly, individual modalities images are used separately for human face recognition. Secondly, fused face images using the proposed method are used for recognition purpose. The highest level of accuracy achieved by using the proposed method is about 98.42%. Lastly, the three existing fusion methods are applied on the same face database for comparison with the results of the proposed method. All the result...
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Papers by Consuelo Gonzalo