Image fusion is the process of combining information from two or more images into a single composite image that is
more informative for visual perception or additional processing. Pan-sharpening algorithms work either in the spatial or
in the transform domain and the most popular and effective methods include arithmetic combinations (Brovey
transform), the intensity-hue-saturation transform (IHS), principal component analysis (PCA) and different multiresolution
analysis-based methods, typically wavelet transforms. In recent years, a number of image fusion quality
assessment metrics have been proposed. Automatic quality assessment is necessary to evaluate the possible benefits of
fusion, to determine an optimal setting of parameters, as well as to compare results obtained with different algorithms to
check the improvement of spatial resolution while preserving the spectral content of the data. This work addresses the
challenging topic of the quality evaluation of pan-sharpening methods. In particular, a database with a synthetic image
and real GeoEye satellite data was created and several pan-sharpening methods were implemented and tested. Some
interesting results about the color and the spatial distortions of each method were presented and it was demonstrated that
some colors bands are more affected than others depending on the fusion techniques. After the evaluation of these fusion
algorithms, we can conclude that, in general, the à trous wavelet-based methods achieve the best spectral performance
while the IHS-based techniques attain the best spatial accuracy.
The emergence of high-resolution satellites with new spectral channels and the ability to change its viewing angle has highlighted the importance of modeling the atmospheric effects. So, atmospheric correction serves a critical role in the processing of remotely sensed image data, particularly with respect to identification of pixel content. Efficient and accurate realization of images in units of reflectance, rather than radiance, has proven to be a crucial point in the pre-processing of images in remote sensing applications, acquired under a variety of measurement conditions. However, reflectance of the objects recorded by satellite sensors is generally affected by atmospheric absorption and scattering, sensor-targetillumination geometry, and sensor calibration. These normally result in distortion of the actual reflectance of the objects that subsequently affects the extraction of information from images. The use of atmospheric models has significantly improved the results of the corrections. In this study we have proceeded to make the atmospheric correction of the eight multispectral bands of high resolution WorldView-2 satellite by three different atmospherics models (COST, DOS, 6S) defining the geometry of the satellite observation, viewing angle and setting the weather conditions more suited for the acquired images of the study area (Granadilla, Canary Islands). For this purpose, the reflectance obtained by COST, DOS and 6S atmospheric correction techniques are compared with the Top of Atmosphere (TOA) reflectance. Specifically, the 6S atmospheric correction model, based on radiative transfer theory, provides patterns which describe properly atmospheric conditions in this specific study area for monitoring turbid coastal environments. To check the proper functioning of the atmospheric correction comparison was performed between ground-based measurements and corresponding obtained by the eight multispectral satellite channels through the 6S atmospheric model, with similar date, weather and lighting conditions.
Satellite remote sensing is providing a systematic, synoptic framework for advancing scientific knowledge of the Earth
as a complex system of geophysical phenomena that, directly and through interacting processes, often lead to natural
hazards. The recent eruption of a submarine volcano at the El Hierro Island has provided a unique and outstanding
source of tracer that may allow us to study a variety of structures. The island off the Atlantic coast of North Africa—built
mostly from a shield volcano—has been rocked by thousands of tremors and earthquakes since July 2011, and an
underwater volcanic eruption 300 meters below sea level started on October 10, 2011. Thanks to this natural tracer
release, low and high-resolution satellite images obtained from MODIS, MERIS and WorldView sensors have been
processed to provide information on the concentration of a number of marine parameters: chlorophyll, phytoplankton,
suspended matter, yellow substance, CDOM, particulate organic and inorganic, etc. This oceanographic remote sensing
data has played, as well, a fundamental role during field campaigns guiding the Spanish government oceanographic
vessel to the appropriate sampling areas. This paper illustrates the capabilities of satellite remote sensing systems to
improve the understanding of submarine volcanic processes and hazards by providing more frequent observations and
scientific information at a wide variety of wavelengths.
The singular characteristics of the Canarian archipelago (Spain) and, in particular, of the Gran Canaria island have allowed the development of a unique biological richness. Almost half of its territory is protected to preserve the natural environment and, in consequence, the monitoring of vegetated regions plays an important role for regional administrations which aim to develop the corresponding policies for the conservation of such ecosystems. The Normalized Difference Vegetation Index (NDVI) is a common index applied for vegetation studies. It is important to emphasize that NDVI is sensor-dependent, and changes are affected by soil background, irradiance, solar position, atmospheric attenuation, season, hydric situation and climate of the area. So, a fixed threshold cannot be set, even for the same sensor or season, to properly segment vegetated areas. In this context, a robust methodology has been applied to ensure a reliable estimation of changes using the same sensor in multiple dates or different sensors. To that respect, a supervised procedure is presented consisting on the selection of different regions within each image to precisely map each cover with its associated NDVI values and, in consequence, obtain for each individual image the optimal threshold to properly segment vegetation without the need to perform the complex preprocessing required to estimate the ground reflectivity. On the other hand, fires are an important aspect of an ecosystem and their study, a fundamental task to perform a complete assessment of the environmental and economic damage. In our work we have also analyzed in detail the fire occurring during 2007 and precisely assessed the results.
The development of algorithms for the production of reliable Sea Surface Temperature (SST) data sets from space borne infrared radiometers has been pursued by different agencies since late 1960's. The current state of the art in SST retrieval from space is limited by radiometer window placement, radiometer noise, quality of pre-launch characterization, in-flight calibration quality, viewing geometry and, mainly, atmospheric correction.The correction to eliminate the atmospheric effects is a critical and complicated step in the validation process, because although the satellite observations are approaching sufficient quality for routine use, unfortunately the thermal structure in the upper 10 m of the ocean is complex and highly variable, so SST may be significantly different depending on the vertical depth of the in situ measurement, the local time of day, local conditions at the air-sea interface and the instrument used; so in order to properly merge skin and bulk temperatures, daytime match-ups have been excluded from the validation process. In this context, the validation of the NOAA-AVHRR/3 and TERRA-MODIS atmospheric correction algorithms for the retrieval of sea surface temperature from the Canary Islands-Azores-Gibraltar area is performed by using more than 1500 in situ temperature measurements, derived from the ARGO Data Collection System and by the Oceanographic Service of the University of Las Palmas Gran Canaria, over the period from December 2000 to September 2003.
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.