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.

The eruptive phase of a submarine volcano located 2 km away from the southern coast of El Hierro Island started on October 2011. This extraordinary event provoked a dramatic perturbation of the water column. In order to understand and quantify the environmental impacts caused, a regular multidisciplinary monitoring was carried out using remote sensing sensors. In this context, we performed the systematic processing of every MODIS and MERIS and selected high resolution Worldview-2 imagery to provide information on the concentration of a number of biological, physical and chemical parameters. On the other hand, the eruption provided an exceptional source of tracer that allowed the study a variety of oceanographic structures. Specifically, the Canary Islands belong to a very active zone of long-lived eddies. Such structures are usually monitored using sea level anomaly fields. However these products have coarse spatial resolution and they are not suitable to perform submesoscale studies. Thanks to the volcanic tracer, detailed studies were undertaken with ocean colour imagery allowing, using the diffuse attenuation coefficient, to monitor the process of filamentation and axisymmetrization predicted by theoretical studies and numerical modelling. In our work, a novel 2-step segmentation methodology has been developed. The approach incorporates different segmentation algorithms and region growing techniques. In particular, the first step obtains an initial eddy segmentation using thresholding or clustering methods and, next, the fine detail is achieved by the iterative identification of the points to grow and the subsequent application of watershed or thresholding strategies. The methodology has demonstrated an excellent performance and robustness and it has proven to properly capture the eddy and its filaments.

In this letter, a region-based fusion methodology is presented for joint classification and hierarchical segmentation of specific ground cover classes from high-spatial-resolution remote sensing images. Multispectral information is fused at the partition level using nonlinear techniques, which allows the different relevance of the various bands to be fully exploited. A hierarchical segmentation is performed for each individual band, and the ensuing segmentation results are fused in an iterative and cooperative way. At each iteration, a consensus partition is obtained based on information theory and is combined with a specific ground cover classification. Here, the proposed approach is applied to the extraction and segmentation of vegetation areas. The result is a hierarchy of partitions with the most relevant information of the vegetation areas at different levels of resolution. This system has been tested for vegetation analysis in high-spatial-resolution images from the QuickBird and GeoEye satellites.

Spatial registration of multidate or multisensorial images is required for many applications in remote sensing. Automatic image registration, which has been extensively studied in other areas of image processing, is still a complex problem in the framework of remote sensing. In this work we explore an alternative strategy for a fully automatic and operational registration system capable of registering multitemporal and multisensorial remote sensing satellite images with high accuracy and avoiding the use of ground control points, exploiting the maximum reliable information in both images (coastlines not occluded by clouds), which have been coarsely geometrically corrected only using an orbital prediction model. The automatic feature-based approach is summarized as follows: i) Reference image coastline extraction; ii) Sensed image gradient energy map estimation and iii) Contour matching, mapping function estimation and transformation of the sensed images. Several experimental results for single sensor imagery (AVHRR/3) and multisensorial imagery (AVHRR/3-SeaWiFS-MODIS-ATSR) from different viewpoints and dates 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.