Dionisio Rodríguez-Esparragón

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.

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.

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.

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

PET-CT image fusion using random forest and à-trous wavelet transform

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 indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful.

Experimental Characterization of Close-Emitter Interference in an Optical Camera Communication System

Due to the massive insertion of embedded cameras in a wide variety of devices and the generalized use of LED lamps, Optical Camera Communication (OCC) has been proposed as a practical solution for future Internet of Things (IoT) and smart cities applications. Influence of mobility, weather conditions, solar radiation interference, and external light sources over Visible Light Communication (VLC) schemes have been addressed in previous works. Some authors have studied the spatial intersymbol interference from close emitters within an OCC system; however, it has not been characterized or measured in function of the different transmitted wavelengths. In this work, this interference has been experimentally characterized and the Normalized Power Signal to Interference Ratio (NPSIR) for easily determining the interference in other implementations, independently of the selected system devices, has been also proposed. A set of experiments in a darkroom, working with RGB multi-LED transmitters and a general purpose camera, were performed in order to obtain the NPSIR values and to validate the deduced equations for 2D pixel representation of real distances. These parameters were used in the simulation of a wireless sensor network scenario in a small office, where the Bit Error Rate (BER) of the communication link was calculated. The experiments show that the interference of other close emitters in terms of the distance and the used wavelength can be easily determined with the NPSIR. Finally, the simulation validates the applicability of the deduced equations for scaling the initial results into real scenarios.

A random forest and superpixels approach to sharpen thermal infrared satellite imagery

Thermal infrared (T IR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform. Often, T IR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen T IR imagery to shortwave band pixel resolutions. One of the most classic thermal sharpening technique is T sHARP . It uses a relationship between land surface temperature and normalized vegetation index (N DV I). However, there are several studies that prove that a single relationship between T IR and N DV I may only exist for a limited class of landscape. Our work hypothesis stated that it is possible to improve the spatial resolution of T IR imagery considering a relationship between vegetation and several soil spectral indexes and T IR as well the spatial context information. In this work, the potential of Superpixels (SP ) combined with Regression Random Forest (RRF ) is used to augmenting the spatial resolution of the Landsat 8 T IR (Band 10 and 11) imagery to their visible (V IS) spatial resolution. The SP allows to consider the contextual information over the land cover, and RF allows to integrate in a unique model the relationship between five spectral indices and T IR data. The results obtained by SP-RRF approach shows the potential of this methodology, compared with classical T sHARP method.

Object-based quality evaluation procedure for fused remote sensing imagery

Abstract Satellite sensors usually provide two types of data: panchromatic and multispectral images which are characterized by their high spatial resolution and high spectral resolution respectively. In this context, the fusion techniques or pansharpening consist of merging these different aspects to obtain a fused (or pan-sharpened) image with high spatial and spectral resolutions. In this paper, a new quality assessment scheme for pan-sharpened remote sensing imagery is proposed. The methodology described extracts the segments of the images to constitute the basic elements of the measuring quality methodology. This new strategy overcomes traditional pixel-based perspectives, approaching an evaluation by human observers. The results of its application to a set of fused images show that an object-based assessment is consistent in terms of quality determination of both the spectral and spatial properties of remote sensing images.

Increasing the UAV data value by an OBIA methodology

Recently, there has been a noteworthy increment of using images registered by unmanned aerial vehicles (UAV) in different remote sensing applications. Sensors boarded on UAVs has lower operational costs and complexity than other remote sensing platforms, quicker turnaround times as well as higher spatial resolution. Concerning this last aspect, particular attention has to be paid on the limitations of classical algorithms based on pixels when they are applied to high resolution images. The objective of this study is to investigate the capability of an OBIA methodology developed for the automatic generation of a digital terrain model of an agricultural area from Digital Elevation Model (DEM) and multispectral images registered by a Parrot Sequoia multispectral sensor board on a eBee SQ agricultural drone. The proposed methodology uses a superpixel approach for obtaining context and elevation information used for merging superpixels and at the same time eliminating objects such as trees in order to generate a Digital Terrain Model (DTM) of the analyzed area. Obtained results show the potential of the approach, in terms of accuracy, when it is compared with a DTM generated by manually eliminating objects.

Convolutional neural networks for estimating spatially distributed evapotranspiration

Efficient water management in agriculture requires an accurate estimation of evapotranspiration (ET). There are available several balance energy surface models that provide a daily ET estimation (ETd) spatially and temporarily distributed for different crops over wide areas. These models need infrared thermal spectral band (gathered from remotely sensors) to estimate sensible heat flux from the surface temperature. However, this spectral band is not available for most current operational remote sensors. Even though the good results provided by machine learning (ML) methods in many different areas, few works have applied these approaches for forecasting distributed ETd on space and time when aforementioned information is missing. However, these methods do not exploit the land surface characteristics and the relationships among land covers producing estimation errors. In this work, we have developed and evaluated a methodology that provides spatial distributed estimates of ETd without thermal information by means of Convolutional Neural Networks.