Consuelo Gonzalo-Martín

A GEOBIA Methodology for Fragmented Agricultural Landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

Local optimal scale in a hierarchical segmentation method for satellite images

Over recent decades, remote sensing has emerged as an effective tool for improving agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi-resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchical segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra-variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a single image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.

An Approach to Detect Negation on Medical Documents in Spanish

The adoption of hospital EHR technology is significantly growing and expected to grow. Digitalized information is the basis for health analytics. In particular, patient medical records contain valuable clinical information written in narrative form that can only be extracted after it has been previously preprocessed with Natural Language Processing techniques. An important challenge in clinical narrative text is that concepts commonly appear negated. Though worldwide there are nearly 500 million Spanish speakers, there seems to be no algorithm for negation detection in medical texts written in that language.

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

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.

A machine learning approach for agricultural parcel delineation through agglomerative segmentation

ABSTRACT A correct delineation of agricultural parcels is a primary requirement for any parcel-based application such as the estimate of agricultural subsidies. Currently, high-resolution remote-sensing images provide useful spatial information to delineate parcels; however, their manual processing is highly time consuming. Thus, it is necessary to create methods which allow performing this task automatically. In this work, the use of a machine-learning algorithm to delineate agricultural parcels is explored through a novel methodology. The proposed methodology combines superpixels and supervised classification in order to determine which adjacent superpixels should be merged, transforming the segmentation issue into a machine learning matter. A visual evaluation of results obtained by the methodology applied to two areas of a high-resolution satellite image of fragmented agricultural landscape points out that the use of machine-learning algorithm for this task is promising.

Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage of the world’s total cultivated surface. To characterize and analyze this type of landscape, multispectral (MS) images with high and very high spatial resolutions are required. Multi-source image fusion algorithms are normally used to improve the spatial resolution of images with a medium spatial resolution. In particular, pansharpening (PS) methods allow one to produce high-resolution MS images through a coherent integration of spatial details from a panchromatic (PAN) image with spectral information from an MS. The spectral and spatial quality of source images must be preserved to be useful in RS tasks. Different PS strategies provide different trade-offs between the spectral and the spatial quality of the fused images. Considering that agricultural landscape images contain many levels of significant structures and edges, the PS algorithms based on filtering processes must be scale-aware and able to remove different levels of detail in any input images. In this work, a new PS methodology based on a rolling guidance filter (RGF) is proposed. The main contribution of this new methodology is to produce artifact-free pansharpened images, improving the MS edges with a scale-aware approach. Three images have been used, and more than 150 experiments were carried out. An objective comparison with widely-used methodologies shows the capability of the proposed method as a powerful tool to obtain pansharpened images preserving the spatial and spectral information.

Feature Selection using Particle Swarm Optimization for Thermal Face Recognition

This paper presents an algorithm for feature selection based on particle swarm optimization (PSO) for thermal face recognition. The total algorithm goes through many steps. In the very first step, thermal human face image is preprocessed and cropping of the facial region from the entire image is done. In the next step, scale invariant feature transform (SIFT) is used to extract the features from the cropped face region. The features obtained by SIFT are invariant to object rotation and scale. But some irrelevant and noisy features could be produced with the actual features. Unwanted features have to be removed. In other words, optimum features have to be selected for better recognition accuracy. The PSO helps to identify the optimum features set using local as well as global searches. Here, this process has been implemented to select a subset of features that effectively represents original feature extracted for better classification convergence. Finally, minimum distance classifier is used to find the class label of each testing images. Minimum distance classifier acts as an objective function for PSO. In this work, all the experiments have been performed on UGC-JU thermal face database. The maximum success rate of 98.61 % recognition has been achieved using SIFT and PSO for frontal face images and 90.28 % for all images.

Evaluation of the performance of spatial assessments of pansharpened images

The evaluation of the spatial quality is one of the factors that determine the performance of pansharpening algorithms for remote sensing images. However, the number of studies that focus on the functioning of these measures is not extensive. This paper addresses the evaluation of the performance of spatial assessments of pansharpened images. For this, a test of affine transformations that distorts the image used as reference is designed and implemented. This test is applied to the images of a database created for this purpose. Thus the behavior of different selected spatial indices is obtained. As well as the results of a proposed new spatial index based on the discrete cosine transform. Additionally, spatial quality measurements have been carried out between the panchromatic and pansharpened images in order to test the performance of the spatial quality indices.

Text Analysis and Information Extraction from Spanish Written Documents

Despite of the spread of Electronic Health Records (EHRs) in Spanish hospitals and Spanish occupying the second place in the ranking of number of speakers, to the best of our knowledge there are no natural language processing tools for medical texts written in Spanish.

Fusion of Visible and Thermal Images Using a Directed Search Method for Face Recognition

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. T...