Angel Garcia-Pedrero

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.

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.

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.

Assessment of the spectral quality of fused images using the CIEDE2000 distance

Image fusion (pan-sharpening) plays an important role in remote sensing applications. Mainly, this process allows to obtain images of high spatial and spectral resolution. However, pan-sharpened images usually present spectral and spatial distortion when comparing with the source images. Because of this, the evaluation of the spectral quality of pan-sharpened images is a fundamental subject to optimize and compare the results of different algorithms. Several assessments of spectral quality have been described in the scientific literature. However, no consensus has been reached on which one describes optimally the spectral distortion in the image. In addition, its performance from the point of view of perceived spectral quality has not been addressed. The aim of this paper is to explore the use of CIEDE2000 distance to evaluate the spectral quality of the fused images. To do this, a database containing remote sensing imagery and its fusion products was created. The spectral quality of the imagery on the database was evaluated using both common quantitative indices and CIEDE2000. With the purpose of determining the relationship between the quantitative indices of spectral quality and the subjective perception of the spectral quality of the merged image, these results were compared to the qualitative assessment provided by a mean opinion score test.

Supervoxels-Based Histon as a New Alzheimer’s Disease Imaging Biomarker

Alzheimer’s disease (AD) represents the prevalent type of dementia in the elderly, and is characterized by the presence of neurofibrillary tangles and amyloid plaques that eventually leads to the loss of neurons, resulting in atrophy in specific brain areas. Although the process of degeneration can be visualized through various modalities of medical imaging and has proved to be a valuable biomarker, the accurate diagnosis of Alzheimer’s disease remains a challenge, especially in its early stages. In this paper, we propose a novel classification method for Alzheimer’s disease/cognitive normal discrimination in structural magnetic resonance images (MRI), based on the extension of the concept of histons to volumetric images. The proposed method exploits the relationship between grey matter, white matter and cerebrospinal fluid degeneration by means of a segmentation using supervoxels. The calculated histons are then processed for a reduction in dimensionality using principal components analysis (PCA) and the resulting vector is used to train an support vector machine (SVM) classifier. Experimental results using the OASIS-1 database have proven to be a significant improvement compared to a baseline classification made using the pipeline provided by Clinica software.

TS2uRF: A New Method for Sharpening Thermal Infrared Satellite Imagery

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform. TIR resolution is often not suitable for monitoring crop conditions of fragmented farming lands, e.g., the accurate estimates of evapotranspiration (ET) based on surface energy balance from remote sensing for irrigation water management. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to a shortwave band pixel resolution. However, most methods concentrate on the visual effects of the thermal sharpened images, and they treat the pixels as independent samples without considering their spatial context, which can give rise to adverse effects such as artifacts. In this work, a new thermal sharpening method called TS2uRF is proposed. The potential of superpixels (SP) combined with regression random forest (RRF) have been used to augment the spatial resolution of the Landsat 8 TIR (100 m) imagery to their visible (VIS) spatial resolution (30 m). The SP has allowed the contextual information on the land cover to be integrated, and RRF has allowed the relationship between five spectral indices and TIR data to be integrated into a single model. The TIR sharpened images obtained using the TS2uRF were compared with images obtained using the TsHARP, one of the most classic thermal sharpening techniques, evaluating the root-mean-square error (RMSE) and structural similarity index (SSIM) for measuring image quality. In all of the cases evaluated, the RMSE and SSIM of the images sharpened using the TS2uRF method outperform those obtained using TsHARP. In particular, the TS2uRF method has an average error of 1.14 °C (RMSE) lower than TsHARP, regarding SSIM, TS2uRF outperforms TsHARP on average by 0.218 . From the visual comparison, it has been shown that the TS2uRF methodology avoids the artifacts that appear in the enhanced images using the TsHARP method.