4,154 research outputs found
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping. Up
until recently, this has always dependent on using the human eye. Using
airborne lasers, it has become possible to quickly "see" more of the world in
many more dimensions. The resulting enormous point clouds serve as data sources
for applications far beyond the original mapping purposes ranging from flooding
protection and forestry to threat mitigation. In order to process these large
quantities of data, novel methods are required. In this contribution, we
develop models to automatically classify ground cover and soil types. Using the
logic of machine learning, we critically review the advantages of supervised
and unsupervised methods. Focusing on decision trees, we improve accuracy by
including beam vector components and using a genetic algorithm. We find that
our approach delivers consistently high quality classifications, surpassing
classical methods
Multi-input distributed classifiers for synthetic genetic circuits
For practical construction of complex synthetic genetic networks able to
perform elaborate functions it is important to have a pool of relatively simple
"bio-bricks" with different functionality which can be compounded together. To
complement engineering of very different existing synthetic genetic devices
such as switches, oscillators or logical gates, we propose and develop here a
design of synthetic multiple input distributed classifier with learning
ability. Proposed classifier will be able to separate multi-input data, which
are inseparable for single input classifiers. Additionally, the data classes
could potentially occupy the area of any shape in the space of inputs. We study
two approaches to classification, including hard and soft classification and
confirm the schemes of genetic networks by analytical and numerical results
Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach
Gullies are landforms with specific patterns of shape,
topography, hydrology, vegetation, and soil characteristics. Remote
sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve
as inputs into an iterative algorithm, initialized using a micromapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels
with similar characteristics in a pool of unlabeled data, and gully
objects are detected where high densities of gully pixels are enclosed
by an alpha shape. Gully objects are used in subsequent iterations
following a mechanism where the algorithm uses the most reliable
pixels as gully training samples. The gully class continuously grows
until an optimal scenario in terms of accuracy is achieved. Results
are benchmarked with manually tagged gullies (initial gully labeled
area <0.3% of the total study area) in two different watersheds
(408 and 302 km2, respectively) yielding total accuracies of >98%,
with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation Coefficient >0.5, and receiver operating characteristic
Area Under the Curve >0.89. Hence, our method outlines gullies
keeping low false-positive rates while the classification quality has
a good balance for the two classes (gully/no gully). Results show
the most significant gully descriptors as the high temporal radar
signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (21.8%). This research builds
on previous studies to face the challenge of identifying and outlining
gully-affected areas with a shortage of training data using global
datasets, which are then transferable to other large (semi-) arid
regions.This research is part of the DEM_HYDR2024 project sup ported by TanDEM-X Science Team, therefore the authors
would like to express thanks to the Deutsches Zentrum fĂĽr Luft und Raumfahrt (DLR) as the donor for the used TanDEM-X
datasets. They acknowledge the financial support provided by
the Namibia University of Science and Technology (NUST)
within the IRPC research funding programme and to ILMI for
the sponsorship of field trips to identify suitable study areas.
Finally, they would like to express gratitude toward Heidelberg
University and the Kurt-Hiehle-Foundation for facilitating the
suitable work conditions during this research
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
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