Francisco Eugenio

Automatic satellite image georeferencing using a contour-matching approach

Multitemporal and multisatellite studies or comparisons between satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images. This paper presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows correcting the low-frequency error component, mainly due to timing and orbital model errors, as well as the high-frequency error component, due to variations in the spacecrafts attitude. The approach aims at exploiting the maximum reliable information in the image to guide the matching algorithm. The contour-matching process has three main steps: 1) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; 2) initialization of the contours positions; 3) estimation of the transformation parameters (affine model) using a contour optimization approach. Three different robust and automatic algorithms are proposed for optimization, and their main features are discussed. Finally, the performance of the three proposed algorithms is assessed using a new error estimation technique applied to Advanced Very High Resolution Radiometer (AVHRR), Sea-viewing Wide Field of view Sensor (SeaWiFS), and multisensor AVHRR-SeaWiFS imagery.

Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery

The upward movement of cool and nutrient-rich waters toward the surface leads to horizontal alterations in the distribution of the physical, chemical, and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques; however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, the fact that gradients are weak, the strong morphological variation, and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure extraction. This automatic technique is based on a coarse-segmentation methodology followed by a fine-detail growing process. The complete system has been validated over a database of 378 multisensorial images of years 2000 to 2003, and it has been applied to the detection and feature extraction of coastal upwellings and filaments in three areas with different characteristics, such as the Canary Islands, Cape Ghir, and the Alboran Sea, using imagery from the Advanced Very High Resolution Radiometer 2 and 3 sensors, the Sea-viewing Wide Field-of-view Sensor, and the Moderate Resolution Imaging Spectroradiometer sensor, demonstrating its effectiveness and robustness in a wide variety of climate conditions.

High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery

Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy.

Sea-State Determination Using GNSS-R Data

Global Navigation Satellite Systems (GNSS) signals can be used to infer geophysical data related to the surface where they scatter. When dealing with the sea surface, its state influences the GNSS scattered signals and, therefore, the GNSS reflectometry (GNSS-R) observables. The aim of the Advanced L-band Emissivity and Reflectivity Observations of the Sea Surface 2008 field experiment was to gather experimental data to study the relationship of the GNSS-R delay-Doppler maps (DDMs) and the sea state. This work describes the field campaign and the main results obtained, where among them is the use of the DDM volume as a roughness descriptor weakly affected by the GPS satellite geometry.

Seasonal and temporal study of the northwest African upwelling system

The upwelling index (UI) obtained from sea surface temperature (SST) images for the period 1987–2006 and remote sensing wind stress were used to analyse the features of the coastal upwelling region off northwest Africa. The seasonal distribution shows a persistent upwelling throughout the year from 20° N to 33° N, seasonal behaviour from 12° N to south of 20° N, and an almost total lack of upwelling throughout the year from 5° N to 12° N. The major centres of active upwelling are located around Cape Ghir, Cape Juby and Cape Blanc. The UI shows an intensification of the upwelling system off northwest Africa during the 20-year period while the alongshore wind stress remains almost stable. During this period, upwelled waters off Cape Blanc have increased their offshore spreading.

Motion Estimation Techniques to Automatically Track Oceanographic Thermal Structures in Multisensor Image Sequences

The ocean involves a complex set of physical, chemical, biological, and geological processes, interacting with each other to influence our climate and natural environment. One of the most important disciplines in oceanography is the study of the ocean dynamics and, particularly, the ocean surface circulation. One can estimate this by the automated tracking of thermal infrared features in pairs of sequential satellite imagery. In this context, an extensive analysis of different motion estimation techniques has been performed by employing databases with synthetic sequences, real sequences, and in situ measurements. Four region- based metrics and two differential algorithms are proposed to estimate surface currents in multitemporal and multisensor AVHRR and MODIS image sequences. Once the appropriate motion estimation techniques have been selected, a new methodology to compute ocean currents is proposed. It includes a preliminary step to precisely segment the oceanographic structures and a second step to track its motion using additional modules (initialization, preprocessing, and postprocessing) to increase effectiveness. The information provided by the segmentation step reduces computing times, initializes the motion estimation parameters with appropriate values, and increases the overall performance. In summary, this two-stage approach combines image processing tools and physical oceanography knowledge to achieve a good ocean current estimation.

Automatic Sun Glint Removal of Multispectral High-Resolution Worldview-2 Imagery for Retrieving Coastal Shallow Water Parameters

Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. These challenges need the accurate determination of the water radiance, which is not often valued compared to other sources such as atmosphere and specular water reflection (sun glint). In this context, the atmospheric correction and the glinting removal have demonstrated to be critical steps in the preprocessing chain of high resolution images. In this work, the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is used to compensate the atmospheric effects and to compute part of the deglinting algorithm using the modeled direct normalized irradiance. This paper describes a novel automatic deglinting procedure, integrated in the Radiative Transfer Modeling (RTM) inversion of the shallow water environments, which allows computing the water Inherent Optical Properties (IOPs), bathymetry and seafloor albedo contributions. The proposed methodology has demonstrated a proper performance for environmental monitoring in shallow water areas.

Evaluation of thresholding techniques applied to oceanographic remote sensing imagery

In many image processing applications, the gray levels of pixels belonging to the object are quite different from the levels belonging to the background. Thresholding becomes then a simple but effective tool to separate objects from the background. This segmentation tool is being used in many research and operational applications, so attempts to automate thresholding has been a permanent area of interest. However, several difficulties impede to achieve in all the situations the desired results, so for any specific problem, the different techniques will have to be tested in order to select those providing the best performance. In this paper we have conducted a survey of image thresholding methods with a view to assess their performance when applied to remote sensing images and especially in oceanographic applications. Those algorithms have been categorized into two groups, local and global thresholding techniques, and the global ones again classified according to the information they are exploiting. This classification has lead to histogram shape-based methods, clustering-based methods, entropy-based methods, object attribute-based methods and spatial methods. After the application of a total of 36 techniques to visible, IR and microwave (synthetic aperture radar) remote sensing images, the optimum methods for each one have been selected.

A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery

An automatic approach for high accuracy registration of multisensor and multitemporal remote sensing images is presented. It avoids the use of ground control points, while exploiting the maximum reliable information in both images. Features to be used for image registration are those contours in both images that have been classified as coastline (reliable information). The automatic contour-based approach is summarized by the following steps: (i) reference image coastline extraction; (ii) sensed image gradient energy map estimation; (iii) contour matching, mapping function estimation and transformation of the sensed images. The algorithm proposed is automatic and of significant value in an operational context. Several experimental results for single sensor imagery (AVHRR) from different viewpoints and dates as well as multisensor imagery (AVHRR-SeaWiFS) have verified the robustness and accuracy of the proposed automatic registration algorithm, demonstrating its capability of registering satellite images of coastal areas within one pixel.

Cloud motion estimation in SEVIRI image sequences

Determination of atmospheric dynamic characteristics from remote sensing imagery is fundamental in weather and climate studies. The SEVIRI radiometer, on board the MSG, with its 12 bands and 15 minutes sensing capability provides an important amount of information for cloud tracking. In this work, we have first conducted a detailed evaluation of twelve region matching techniques in order to select those providing the best results. For this performance evaluation, databases of synthetic and real sequences have been used. Next, the best metrics have been incorporated in a new methodology that includes a preliminary stage that segments cloudy structures to initialize the optimum motion estimation parameters (template size and search window dimensions). Also a study region mask is generated to disable the application of the motion estimation algorithm in unreliable areas, thus, eliminating erroneous vectors and decreasing the computation times.