SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art

Research on robust salient object extraction in image

制度:新 ; 文部省報告番号:甲2641号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS–InSAR)

Many factors can influence the displacements of a dam, including water level variability and environmental temperatures, in addition to the dam composition. In this work, optical-based classification, thermal diachronic analysis, and a quasi-PS (Persistent Scatter) Interferometric SAR technique have been applied to determine both forcing factors and resulting displacements of the crest of the Castello dam (South Italy) over a one-year time period. The dataset includes Sentinel-1A images acquired in Interferometric Wide swath mode using the Terrain Observation with Progressive Scans SAR (TOPSAR); Landsat 8 Thermal Infrared Sensor (TIRS) thermal images, and Global Navigation Satellite System (GNSS) for interpreting the motion of the top of the dam retrieved via interferometry. Results suggest that it is possible to monitor both dam water level and temperature periodic forcing factors and resulting displacements via a synergistic use of different satellite images

Toward a comprehensive dam monitoring: On-site and remote-retrieved forcing factors and resulting displacements (gnss and ps–insar)

Many factors can influence the displacements of a dam, including water level variability and environmental temperatures, in addition to the dam composition. In this work, optical-based classification, thermal diachronic analysis, and a quasi-PS (Persistent Scatter) Interferometric SAR technique have been applied to determine both forcing factors and resulting displacements of the crest of the Castello dam (South Italy) over a one-year time period. The dataset includes Sentinel-1A images acquired in Interferometric Wide swath mode using the Terrain Observation with Progressive Scans SAR (TOPSAR); Landsat 8 Thermal Infrared Sensor (TIRS) thermal images, and Global Navigation Satellite System (GNSS) for interpreting the motion of the top of the dam retrieved via interferometry. Results suggest that it is possible to monitor both dam water level and temperature periodic forcing factors and resulting displacements via a synergistic use of different satellite images

Temporal Characteristics of P-band Tomographic Radar Backscatter of a Boreal Forest

Temporal variations in synthetic aperture radar (SAR) backscatter over forests are of concern for any SAR mission with the goal of estimating forest parameters from SAR data. In this article, a densely sampled, two-year long time series of P-band (420 to 450 MHz) boreal forest backscatter, acquired by a tower-based radar, is analyzed. The experimental setup provides time series data at multiple polarizations. Tomographic capabilities allow the separation of backscatter at different heights within the forest. Temporal variations of these multi-polarimetric, tomographic radar observations are characterized and quantified. The mechanisms studied are seasonal variations, effects of freezing conditions, diurnal variations, effects of wind and the effects of rainfall on backscatter. An emphasis is placed on upper-canopy backscatter, which has been shown to be a robust proxy for forest biomass. The canopy backscatter was most sensitive to freezing conditions but was more stable than ground-level backscatter and full-forest backscatter during non-frozen conditions. The analysis connects tree water transport mechanisms and P-band radar backscatter for the first time. The presented results are useful for designing boreal forest parameter estimation algorithms, using data from P-band SARs, that are robust to temporal variations in backscatter. The results also present new forest remote sensing opportunities using P-band radars

Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

A Novel Statistical-based Approach for 3-D Surface Detection

The field of medical image analysis is concerned with the extraction of salient information from complex digital imagery. Developments in image acquisition tools has given rise to a number of 2-D and 3-D digital image domains that are capable of mapping the anatomy and internal structures of a patient in an non-invasive fashion. The data produced by these tools is inherently complex, and a number of image processing techniques are commonly applied to simplify the images in order to extract the information deemed most relevant. Image segmentation is one commonly applied process which partitions a digital image into multiple segments that correspond to various structures within the data. The goal of segmentation is to change the representation of an image into something that is easier to visualise and analyse in complex tasks (i.e. targeted clinical treatment planning). Often these segmentation tasks are performed manually by an expert clinician, however the task of drawing object contours is a time consuming process subject to human biases and interpretation. Automated and semi-automated segmentation is a complex, non-trivial process reliant on a number of pre-processing stages which first extract the spatial structural information contained within the image. For 2-D images, the structural information is contained within edge features and there are a number of edge detection algorithms in the literature which have been extensively appraised. For 3-D images this structural information is contained within the surface features, and while surface detection algorithms exist, their development is immature compared to edge detection and formal evaluation in the literature is largely absent. Furthermore, recent developments in statistical methods for 2-D edge feature extraction have showed promise in resolving 2-D structural information in medical data, however no work has yet explored these approaches in 3-D. In this thesis two novel methods of statistical surface detection are presented, which contribute to the field by transferring approaches of 2-D statistical edge detection into3-D. The proposed methods optimise the resolving power of the 2-D statistical methods while providing accurate surface detection in the x, y and z dimensions of images. The methods are presented with a range of parametric and non-parametric statistical tests which were extensively analysed using both qualitative and objective methods. In addition, the framework for evaluation is an additional novel contribution in this work, which considers individual aspects of surface detection performance, such as the effects of the statistical properties of the regions within the image, the impact of surface topology, and the response to multiple distinct regions present within the image. A comprehensive dataset of controlled interfaces is developed, and performance of the surface detection algorithms were judged using a novel fast implementation of F-measure analysis against ground truth solutions which is new to this work. The surface detection methods are also analysed on real MRI data, and their performance was qualitatively assessed on the ability to detect brain tumour boundaries and structural pathologies in paediatric patient data. For a comparison against the state of art in surface detection, the methods evaluated in the thesis were compared against two existing baseline approaches, namely the 3-D Canny method, and 3-D Steerable filters. The results of the evaluations reveal that the proposed 3-D statistical method for surface detection offers improved detection of surfaces on synthetic data with varying interface and topology considerations. Furthermore, the proposed methods improve detection of surfaces when the variability in image intensity high, such as within regions of texture, which is suitable for delineating regions of complex structure in MRI data. Additionally, the statistical methods were able to match the performance of the baseline methods under conditions considered optimal for the baseline approaches, such as the detection of surfaces with a strong intensity differential. Furthermore, the proposed methods of surface detection are shown to be suitable for real 3-D data which is anisotropic in resolution, namely on MRI imagery where the z-spacing within the dataset is often of poor resolution. Provided as a recommendation for further work, the best performing techniques are presented, notably these were the χ2 and Student t-test statistical methods. Characteristically these methods produced strong magnitude surfaces with good connectivity on real and synthetic data, with the χ2 test also achieving a good suppression of image noise. Therefore, illustrating the potential of this novel method of 3-D surface detection for medical image analysis applications

Ecohydrology in water-limited environment using quantitative remote sensing - the Heihe River basin (China) case

Water-limited environments exist on all continents of the globe and they cover more than 30% of the Earth’s land surface. The eco-environments of these regions tend to be fragile and they are changing in a dramatic way through processes like land desertification, shrinking of oases, groundwater depletion, and soil erosion. These are either human induced or results of a changing climate. Implications of these changes for both the regional hydrologic cycle and the vegetation have been documented. Since these changes occur over a wide range of scales in space and time, remote sensing methods are needed to monitor the land surface characteristics, to observe changes in vegetation and hydrological states, and to compare these with predictions from hydrological models. It is widely accepted that remote sensing methods offer the ability to acquire spatially continuous measurements over large areas. Remote sensing can also help to visualize complex processes because the spatial data can be captured regularly over time. China is one of several countries with large arid and semi-arid areas. The Heihe River basin, situated in the arid inland of northwestern China, is one of the areas severely affected by ecoenvironmental degradation and recovery. The problem of the degraded environment is due to overexploitation of surface and ground water leading to shrinking of oases, including the decline and death of natural vegetation, and the lowering of the groundwater table. Exhaustive (over-)use of water resources is the main cause of land degradation in the lower reaches of the basin, called the Ejina oasis. The whole Heihe River basin is therefore selected as study area in this thesis to analyze the long-term eco-environmental changes. What happens in this river basin is likely to have a growing influence on regional hydrological cycles, even affecting human life. Effective management of eco-environmental problems in this critical zone of water-limited conditions will provide scientific evidence for protecting and improving the eco-environment in these Chinese northwestern arid regions, eventually resulting in land improvement. Studies on quantifying the relationship between the vegetation and the water resources are a critical step in developing an ecohydrological approach to resources management in order to minimize environmental degradation. Remote sensing measurements can help us to better understand the effects of changes in water management on hydrological processes and their subsequent feedback to the eco-environment at the regional scale. Remote sensing methods can also provide information to quantify heterogeneity and change at a large scale. Therefore, the main objective of this thesis is to develop a methodology for the quantitative assessment of eco-environmental changes at a large scale in arid regions by integrating remote sensing methods in ecohydrological approaches. Chapter 1 outlines the significance of quantitative assessment of eco-environmental changes using remote sensing methods and applying them for ecohydrology in northwestern China, resulting in the specific research objectives of this thesis. Chapter 2 quantifies both the vertical and horizontal distribution of vegetation in the Qilian Mountains area, representing the upper reaches of the Heihe River basin, based on MODIS NDVI images from the year 2000 - 2006. Our analysis reveals that elevation and aspect are two important impact factors for the vertical distribution of vegetation in a mountainous area. The NDVI increases with the elevation and reaches a maximum value at a certain elevation threshold, and then decreases as the elevation increases beyond this threshold. The optimal vegetation growth is on the shady side of the mountains because of less evapotranspiration. The best combination of temperature and precipitation is assessed providing good conditions for vegetation growth. Chapter 3 presents an efficient method to estimate the regional annual evapotranspiration (ET) based on the SEBS algorithm (Surface Energy Balance System) in the Zhangye basin, representing the middle reaches of the Heihe River basin. The method proposed is a combination of the daily SEBS results and data collected by meteorological stations. The result shows that the annual ET increased gradually during the period 1990-2004 and the main impact factor on the long-term increase of annual ET was the vegetation change. The accuracy of the ET result is validated using a water balance for the whole watershed and the validation reveals that the SEBS algorithm can be used to effectively estimate annual ET in the Zhangye basin. Chapter 4 establishes the quantitative relationship between the runoff of the Heihe River and the long-term vegetation change of the Ejina oasis, located in the lower reaches of the Heihe River. In this part, two time periods are distinguished corresponding to before and after the implementation of a new water allocation scheme in the Heihe River basin. The GIMMS NDVI and MODIS NDVI data sets are used to quantify the long-term change of the oasis vegetation in the first period 1989-2002 and the second period 2000-2006, respectively. The vegetation change shows a decreasing trend from 1989 to 2002 and an increasing trend between 2000 and 2006. Good relation between the runoff of the river and the vegetation growth are found at both stages and the time lag of the observed hysteresis effect of the runoff of the river on the oasis vegetation is one year. In addition, the yearly smallest water amount which sustains the demand of the eco-environment of the Ejina area is estimated to be 4×108 m3 based on MODIS images. Chapter 5 explores a method to quantify the effect of the groundwater depth on the vegetation growth in the year 2000 in the oasis area by combining MODIS NDVI with groundwater observation data. The result demonstrates that the groundwater depth suitable for vegetation growth in this region ranges from 2.8 to 5 m, depending on species composition. Hardly any vegetation growth occurs when the groundwater depth is below 5 m because the rooting depth of the occurring species is limited and cannot maintain adequate water supplies to their canopies when the water depth is below 5 m. The situation changes after implementation of the new water allocation scheme since 2000. The mean NDVI increased and the annual conversion of bare land into vegetated land is about 38 km2 per year during the period 2000 – 2008. It reflects a potential recovery of the eco-environment of the Ejina area. Chapter 6 comprises the main conclusions and the outlook for possible improvements in future research. The main contribution of this study is the successful integration of remote sensing with ecohydrology in quantifying the relationship between water resources and vegetation occurrence at large scale. It provides a methodology to evaluate the long-term vegetation change and the water resources impact using remote sensing data in water-limited areas. The approach of vegetation dynamics, runoff and groundwater impacts presented in this thesis serves as a sound foundation for predicting the effects of future environmental changes. <br/

Sustainable multifunctional materials with tailored magnetic and electrical properties for electronics applications

193 p.En el marco de la transición tecnológica impulsada por la creciente digitalización de la sociedad y la economía, existe una necesidad urgente de materiales altamente eficientes adecuados para sensores y actuadores, que también comprendan paradigmas de sostenibilidad.En este ámbito, se han desarrollado compuestos biodegradables multifuncionales avanzados basados en polímeros naturales o sintéticos. Estos materiales se procesaron en disolución con el objetivo de hacerlos aptos para tecnologías de impresión de fabricación aditiva en base solvente. Se han caracterizado las propiedades físico-químicas y mecánicas de estos materiales, así como sus propiedades funcionales en relación con su concentración. En general, los materiales que presentan funcionalidades magnéticas y eléctricas se han preparado para aplicaciones de sensores y actuadores. La actividad magnética se introdujo incorporando ferrita de cobalto (CoFe2O3) y magnetita (Fe3O4). Ambos son ferritas con magnetización de alta saturación y campos coercitivos relativamente bajos, interesantes para desarrollar sensores y actuadores magnéticos sin contacto. Respecto a los composites con propiedades eléctricas, la funcionalidad se adapta mediante cargas y aditivos de diferente naturaleza: grafito, nanocristales de celulosa (CNCs), y el líquido iónico (IL) 2-hidroxi-etil-trimetilamonio dihidrógeno fosfato [Ch][DHP]