- PE&RC (8)
- Biometris (WU MAT) (4)
- Laboratory of Geo-information Science and Remote Sensing (4)
- Mathematical and Statistical Methods - Biometris (4)
- Hydrology and Quantitative Water Management (1)
- G.T. Alckmin (1)
- Douglas E. Alsdorf (1)
- Trenton E. Franz (1)
- Niko E.C. Verhoest (1)
- Matthew F. McCabe (1)
- P.F. Fischer (1)
- P. Fisher (1)
- Diego G. Miralles (1)
- Rasmus Houborg (1)
- V.G. Jetten (1)
- S.M. Jong de (2)
- L. Kooistra (1)
- E. Koster (2)
- A. Lucieer(older publications) (4)
- Arko Lucieer (1)
- A. Lucieer (4)
- M. Molenaar (1)
- R. Rawnsley (1)
- Matthew Rodell (1)
- A. Stein (4)
- Remko Uijlenhoet (1)
- Wolfgang Wagner (1)
- Q. Zhan (1)
Feature filtering and selection for dry matter estimation on perennial ryegrass: A case study of vegetation indices
Alckmin, G.T. ; Kooistra, L. ; Lucieer, A. ; Rawnsley, R. - \ 2019
In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 1827 - 1831.
Biomass - Collinearity - Dry Matter - Feature Selection - Machine Learning - Pasture - Perennial Ryegrass - Vegetation Indices
Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n Combining double low line 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation > |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE Combining double low line 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.).
The future of Earth observation in hydrology
McCabe, Matthew F. ; Rodell, Matthew ; Alsdorf, Douglas E. ; Miralles, Diego G. ; Uijlenhoet, Remko ; Wagner, Wolfgang ; Lucieer, Arko ; Houborg, Rasmus ; Verhoest, Niko E.C. ; Franz, Trenton E. - \ 2017
Hydrology and Earth System Sciences 21 (2017)7. - ISSN 1027-5606 - p. 3879 - 3914.
In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3- 5 m) resolution sensing of the Earth on a daily basis. Startup companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via highaltitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.
Multivariate Texture segmentation of high-resolution remotely sensed imagery for identification of fuzzy objects
Lucieer, A. ; Stein, A. ; Fisher, P. - \ 2005
International Journal of Remote Sensing 26 (2005)14. - ISSN 0143-1161 - p. 2917 - 2936.
feature distributions - land-cover - classification - patterns
In this study, a segmentation procedure is proposed, based on grey¿level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey¿scale and colour textures, depicted by accuracy values of 96% and 98%, respectively. The second case study involved segmentation of coastal land cover objects from a multi¿spectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP¿based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty
Texture-based landform segmentation of LiDAR Imagery
Lucieer, A. ; Stein, A. - \ 2005
International Journal of applied Earth Observation and Geoinformation (2005). - ISSN 0303-2434 - p. 261 - 270.
classification - objects
In this study, we implement and apply a region growing segmentation procedure based on texture to extract spatial landform objects from a light detection and ranging (LiDAR) digital surface model (DSM). The local binary pattern (LBP) operator, modeling texture, is integrated into a region growing segmentation algorithm to identify landform objects. We apply a multi-scale LBP operator to describe texture at different scales. The paper is illustrated with a case study that involves segmentation of coastal landform objects using a LiDAR DSM of a coastal area in the UK. Landform objects can be identified with the combination of a multi-scale texture measure and a region growing segmentation. We show that meaningful coastal landform objects can be extracted with this algorithm. Uncertainty values provide useful information on transition zones or fuzzy boundaries between objects
Fuzzy object indentification using texture based segmentation of high-resolution DEM and remote sensing imagery of a coastal area in England
Lucieer, A. ; Fischer, P.F. ; Stein, A. - \ 2003
In: Proceedings of the 2nd international symposium on spatial data quality, Hong Kong, 19-20 March 2003. - Hong Kong : The Hong Kong Polytechnic University - p. 296 - 308.
Existential uncertainty of spatial objects segmented from satellite sensor imagery
Lucieer, A. ; Stein, A. - \ 2002
IEEE Transactions on Geoscience and Remote Sensing 40 (2002)1. - ISSN 0196-2892 - p. 2518 - 2521.
This research addresses existential uncertainty of spatial objects derived from satellite sensor imagery. An image segmentation technique is applied at various values of splitting and merging thresholds. We test the hypothesis that objects occurring at many segmentation steps have less existential uncertainty than those occurring at only a few steps
|Pixel unmixing at the sub-pixel scale based on land cover class probabilities : application to urban areas
Zhan, Q. ; Molenaar, M. ; Lucieer, A. - \ 2002
In: Uncertainty in Remote Sensing and GIS / Foody, G., Atkinson, P., London : John Wiley and Sons - p. 59 - 76.
|The DAIS La Peyne experiment : using the optical and thermal DAIS bands to survey and model the surface temperature
Koster, E. ; Lucieer, A. ; Jong, S.M. de; Jetten, V.G. - \ 2000
In: Proceedings XIXth Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS) : ISPRS, Amsterdam, 2000. - Amsterdam : International Archives of Photogrammetry and Remote Sensing, 2000 - p. 347 - 354.
|The DIAS LA Peyne Experiment : Using the Optical and Thermal DAIS Bands to Survey and Model the Surface Temperature
Jong, S.M. de; Lucieer, A. ; Koster, E. - \ 2000
Unknown Publisher - 115 p.