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.

Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots. INDEX TERMS Convolutional neural network, deep learning, edge extraction, land parcel identification system, parcels delineation.

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.

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 comparison of the results provided by the proposed methodology with field-based data (ground truth) shows that the use of segmentation consensus is promising for outlining agricultural plots in a semi-supervised manner.

Abstract:The Fractal Dimension (FD) of an image defines the roughness using a real number which is highly associated with the human perception of surface roughness. It has been applied successfully for many computer vision applications such as texture analysis, segmentation and classification. Several techniques can be found in literature to estimate FD. One such technique is Differential Box Counting (DBC). Its performance is influenced by many parameters. In particular, the box height is directly related to the gray-level variations over image grid, which badly affects the performance of DBC. In this work, a new method for estimating box height is proposed without changing the other parameters of DBC. The proposed box height has been determined empirically and depends only on the image size. All the experiments have been performed on simulated Fractal Brownian Motion (FBM) Database and Brodatz Database. It has been proved experimentally that the proposed box height allow to improve the performance of DBC, Shifting DBC, Improved DBC and Improved Triangle DBC, which are closer to actual FD values of the simulated FBM images.

Abstract. Climate evolves following natural variability, and knowledge of these trends is of paramount importance to understand future scenarios in the frame of global change. Obtaining local data is also of importance since climatic anomalies depend on the geographical area. In this sense, the Canary Current is located in one of the major eastern boundary current systems and is mainly driven by the trade winds. The latter promote Ekman transport and give rise to one of the most important upwelling zones of the world on the northwest African coast. Nearly 30 years ago, Bakun (1990) raised a hypothesis contending that coastal upwelling in eastern boundary upwelling systems (EBUSs) might be intensified by global warming due to the enhancement of the trade winds, increasing pressure differences between the ocean and the continent. Using available NCEP/NCAR wind data north of the Canary Islands from 1948 to 2017, we show that trade wind intensity experienced a net decrease of 1 m s−1. Moreover, these winds are strongly influenced, as expected, by large-scale atmospheric patterns such as the North Atlantic Oscillation (NAO). In addition, we found a relationship between the wind pattern and the Atlantic Multidecadal Oscillation (AMO), indicating that the ocean contributes to multidecadal atmospheric variability in this area of the ocean with a considerable lag (>10 years).

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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.