Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Automated extraction of road network from medium-and high-resolution images

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. © Pleiades Publishing, Inc. 2006.

Detection of highways in high resolution images using Mathematical Morphology techniques

This paper seeks to apply a routine for highways detection through the mathematical morphology tools in high resolution image. The Mathematical Morphology theory consists of describing structures geometric presents quantitatively in the image (targets or features). This explains the use of the Mathematical Morphology in this work. As high resolution images will be used, the largest difficulty in the highways detection process is the presence of trees and automobiles in the borders tracks. Like this, for the obtaining of good results through the use of morphologic tools was necessary to choose the structuring element appropriately to be used in the functions. Through the appropriate choice of the morphologic operators and structuring elements it was possible to detect the highways tracks. The linear feature detection using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating.

Characterization of the Abydos region through OSIRIS high-resolution images in support of CIVA measurements

Context. On 12 November 2014, the European mission Rosetta delivered the Philae lander on the nucleus of comet 67P /Churyumov-Gerasimenko (67P). After the first touchdown, the lander bounced three times before finally landing at a site named Abydos. Aims. We provide a morphologically detailed analysis of the Abydos landing site to support Philae's measurements and to give context for the interpretation of the images coming from the Comet Infrared and Visible Analyser (CIVA) camera system onboard the lander. Methods. We used images acquired by the OSIRIS Narrow Angle Camera (NAC) on 6 December 2014 to perform the analysis of the Abydos landing site, which provided the geomorphological map, the gravitational slope map, the size-frequency distribution of the boulders. We also computed the albedo and spectral reddening maps. Results. The morphological analysis of the region could suggest that Philae is located on a primordial terrain. The Abydos site is surrounded by two layered and fractured outcrops and presents a 0.02 km(2) talus deposit rich in boulders. The boulder size frequency distribution gives a cumulative power-law index of 4.0 + 0.3/0.4, which is correlated with gravitational events triggered by sublimation and /or thermal fracturing causing regressive erosion. The average value of the albedo is 5.8% at lambda(1) = 480.7 nm and 7.4% at lambda(2) = 649.2 nm, which is similar to the global albedos derived by OSIRIS and CIVA, respectively.

Morphological routine proposal to extraction of highways in digital images of high resolution

This research proposes to apply techniques of Mathematics Morphology to extract highways in digital images of high resolution, targeting the upgrade of cartographic products. Remote Sensing data and Mathematical Morphological techniques were integrated in the process of extraction. Mathematical Morphology's objective is to improve and extract the relevant information of the visual image. In order to test the proposed approach some morphological operators related to preprocess, were applied to the original images. Routines were implemented in the MATLAB environment. Results indicated good performances by the implemented operators. The integration of the technologies aimed to implement the semiautomatic extraction of highways with the purpose to use them in processes of cartographic updating.

Diving into the depth of primary motor cortex: a high-resolution investigation of the motor system using 7Tesla fMRI

Dissertação para a obtenção do Grau de Mestre em Engenharia Biomédica

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

On the acquisition of high-resolution maps with open source software and commercials off-the-shelf quad-rotors

Mosaics are high-resolution images obtained aerially and employed in several scientific research areas, such for example, in the field of environmental monitoring and precision agriculture. Although many high resolution maps are obtained by commercial demand, they can also be acquired with commercial aerial vehicles which provide more experimental autonomy and availability. For what regard to mosaicing-based aerial mission planners, there are not so many - if any - free of charge software. Therefore, in this paper is presented a framework designed with open source tools and libraries as an alternative to commercial tools to carry out mosaicing tasks.

High-resolution bulk density images, using calibrated X-ray radiography of impregnated soil slices

Bulk density of undisturbed soil samples can be measured using computed tomography (CT) techniques with a spatial resolution of about 1 mm. However, this technique may not be readily accessible. On the other hand, x-ray radiographs have only been considered as qualitative images to describe morphological features. A calibration procedure was set up to generate two-dimensional, high-resolution bulk density images from x-ray radiographs made with a conventional x-ray diffraction apparatus. Test bricks were made to assess the accuracy of the method. Slices of impregnated soil samples were made using hardsetting seedbeds that had been gamma scanned at 5-mm depth increments in a previous study. The calibration procedure involved three stages: (i) calibration of the image grey levels in terms of glass thickness using a staircase made from glass cover slips, (ii) measurement of ratio between the soil and resin mass attenuation coefficients and the glass mass attenuation coefficient, using compacted bricks of known thickness and bulk density, and (iii) image correction accounting for the heterogeneity of the irradiation field. The procedure was simple, rapid, and the equipment was easily accessible. The accuracy of the bulk density determination was good (mean relative error 0.015), The bulk density images showed a good spatial resolution, so that many structural details could be observed. The depth functions were consistent with both the global shrinkage and the gamma probe data previously obtained. The suggested method would be easily applied to the new fuzzy set approach of soil structure, which requires generation of bulk density images. Also, it would be an invaluable tool for studies requiring high-resolution bulk density measurement, such as studies on soil surface crusts.

Segmentation of striatal brain structures from high resolution pet images

Dissertation presented at the Faculty of Science and Technology of the New University of Lisbon in fulfillment of the requirements for the Masters degree in Electrical Engineering and Computers

High-resolution seismic images of potentially seismogenic structures beneath the northwest Canterbury Plains, New Zealand

The transpressional boundary between the Australian and Pacific plates in the central South Island of New Zealand comprises the Alpine Fault and a broad region of distributed strain concentrated in the Southern Alps but encompassing regions further to the east, including the northwest Canterbury Plains. Low to moderate levels of seismicity (e. g., 2 > M 5 events since 1974 and 2 > M 4.0 in 2009) and Holocene sediments offset or disrupted along rare exposed active fault segments are evidence for ongoing tectonism in the northwest plains, the surface topography of which is remarkably flat and even. Because the geology underlying the late Quaternary alluvial fan deposits that carpet most of the plains is not established, the detailed tectonic evolution of this region and the potential for larger earthquakes is only poorly understood. To address these issues, we have processed and interpreted high-resolution (2.5 m subsurface sampling interval) seismic data acquired along lines strategically located relative to extensive rock exposures to the north, west, and southwest and rare exposures to the east. Geological information provided by these rock exposures offer important constraints on the interpretation of the seismic data. The processed seismic reflection sections image a variably thick layer of generally undisturbed younger (i.e., < 24 ka) Quaternary alluvial sediments unconformably overlying an older (> 59 ka) Quaternary sedimentary sequence that shows evidence of moderate faulting and folding during and subsequent to deposition. These Quaternary units are in unconformable contact with Late Cretaceous-Tertiary interbedded sedimentary and volcanic rocks that are highly faulted, folded, and tilted. The lowest imaged unit is largely reflection-free Permian Triassic basement rocks. Quaternary-age deformation has affected all the rocks underlying the younger alluvial sediments, and there is evidence for ongoing deformation. Eight primary and numerous secondary faults as well as a major anticlinal fold are revealed on the seismic sections. Folded sedimentary and volcanic units are observed in the hanging walls and footwalls of most faults. Five of the primary faults represent plausible extensions of mapped faults, three of which are active. The major anticlinal fold is the probable continuation of known active structure. A magnitude 7.1 earthquake occurred on 4 September 2010 near the southeastern edge of our study area. This predominantly right-lateral strike-slip event and numerous aftershocks (ten with magnitudes >= 5 within one week of the main event) highlight the primary message of our paper: that the generally flat and topographically featureless Canterbury Plains is underlain by a network of active faults that have the potential to generate significant earthquakes.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover.

A near real-time algorithm for flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management and flood forecasting. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.