4,219 research outputs found
An evolutionary computational based approach towards automatic image registration
Image registration is a key component of various image processing operations
which involve the analysis of different image data sets. Automatic image
registration domains have witnessed the application of many intelligent
methodologies over the past decade; however inability to properly model object
shape as well as contextual information had limited the attainable accuracy. In
this paper, we propose a framework for accurate feature shape modeling and
adaptive resampling using advanced techniques such as Vector Machines, Cellular
Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be
effective in improving feature matching as well as resampling stages of
registration and complexity of the approach has been considerably reduced using
corset optimization The salient features of this work are cellular neural
network approach based SIFT feature point optimisation, adaptive resampling and
intelligent object modelling. Developed methodology has been compared with
contemporary methods using different statistical measures. Investigations over
various satellite images revealed that considerable success was achieved with
the approach. System has dynamically used spectral and spatial information for
representing contextual knowledge using CNN-prolog approach. Methodology also
illustrated to be effective in providing intelligent interpretation and
adaptive resampling.Comment: arXiv admin note: substantial text overlap with arXiv:1303.671
Leveraging Photogrammetric Mesh Models for Aerial-Ground Feature Point Matching Toward Integrated 3D Reconstruction
Integration of aerial and ground images has been proved as an efficient
approach to enhance the surface reconstruction in urban environments. However,
as the first step, the feature point matching between aerial and ground images
is remarkably difficult, due to the large differences in viewpoint and
illumination conditions. Previous studies based on geometry-aware image
rectification have alleviated this problem, but the performance and convenience
of this strategy is limited by several flaws, e.g. quadratic image pairs,
segregated extraction of descriptors and occlusions. To address these problems,
we propose a novel approach: leveraging photogrammetric mesh models for
aerial-ground image matching. The methods of this proposed approach have linear
time complexity with regard to the number of images, can explicitly handle low
overlap using multi-view images and can be directly injected into off-the-shelf
structure-from-motion (SfM) and multi-view stereo (MVS) solutions. First,
aerial and ground images are reconstructed separately and initially
co-registered through weak georeferencing data. Second, aerial models are
rendered to the initial ground views, in which the color, depth and normal
images are obtained. Then, the synthesized color images and the corresponding
ground images are matched by comparing the descriptors, filtered by local
geometrical information, and then propagated to the aerial views using depth
images and patch-based matching. Experimental evaluations using various
datasets confirm the superior performance of the proposed methods in
aerial-ground image matching. In addition, incorporation of the existing SfM
and MVS solutions into these methods enables more complete and accurate models
to be directly obtained.Comment: Accepted for publication in ISPRS Journal of Photogrammetry and
Remote Sensin
Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison
LiDAR point clouds provide rich geometric information, which is particularly
useful for the analysis of complex scenes of urban regions. Finding structural
and semantic differences between two different three-dimensional point clouds,
say, of the same region but acquired at different time instances is an
important problem. A comparison of point clouds involves computationally
expensive registration and segmentation. We are interested in capturing the
relative differences in the geometric uncertainty and semantic content of the
point cloud without the registration process. Hence, we propose an
orientation-invariant geometric signature of the point cloud, which integrates
its probabilistic geometric and semantic classifications. We study different
properties of the geometric signature, which are an image-based encoding of
geometric uncertainty and semantic content. We explore different metrics to
determine differences between these signatures, which in turn compare point
clouds without performing point-to-point registration. Our results show that
the differences in the signatures corroborate with the geometric and semantic
differences of the point clouds.Comment: 18 pages, 6 figures, 1 tabl
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely
sensed hyperspectral image (HSI) which integrates spectral and spatial
information in a unified Bayesian framework. First, we formulate the HSI
classification problem from a Bayesian perspective. Then, we adopt a
convolutional neural network (CNN) to learn the posterior class distributions
using a patch-wise training strategy to better use the spatial information.
Next, spatial information is further considered by placing a spatial smoothness
prior on the labels. Finally, we iteratively update the CNN parameters using
stochastic gradient decent (SGD) and update the class labels of all pixel
vectors using an alpha-expansion min-cut-based algorithm. Compared with other
state-of-the-art methods, the proposed classification method achieves better
performance on one synthetic dataset and two benchmark HSI datasets in a number
of experimental settings
An investigation towards wavelet based optimization of automatic image registration techniques
Image registration is the process of transforming different sets of data into
one coordinate system and is required for various remote sensing applications
like change detection, image fusion, and other related areas. The effect of
increased relief displacement, requirement of more control points, and
increased data volume are the challenges associated with the registration of
high resolution image data. The objective of this research work is to study the
most efficient techniques and to investigate the extent of improvement
achievable by enhancing them with Wavelet transform. The SIFT feature based
method uses the Eigen value for extracting thousands of key points based on
scale invariant features and these feature points when further enhanced by the
wavelet transform yields the best results
Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.Comment: arXiv admin note: text overlap with arXiv:1705.0125
Machine Learning Techniques and Applications For Ground-based Image Analysis
Ground-based whole sky cameras have opened up new opportunities for
monitoring the earth's atmosphere. These cameras are an important complement to
satellite images by providing geoscientists with cheaper, faster, and more
localized data. The images captured by whole sky imagers can have high spatial
and temporal resolution, which is an important pre-requisite for applications
such as solar energy modeling, cloud attenuation analysis, local weather
prediction, etc.
Extracting valuable information from the huge amount of image data by
detecting and analyzing the various entities in these images is challenging.
However, powerful machine learning techniques have become available to aid with
the image analysis. This article provides a detailed walk-through of recent
developments in these techniques and their applications in ground-based
imaging. We aim to bridge the gap between computer vision and remote sensing
with the help of illustrative examples. We demonstrate the advantages of using
machine learning techniques in ground-based image analysis via three primary
applications -- segmentation, classification, and denoising
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse
both low-level and high-level local geometric features for 3D rigid data
matching. It is a common practice to generate distinctive geometric descriptors
by fusing low-level features from various viewpoints or subspaces, or enhance
geometric feature matching by leveraging multiple high-level features. In prior
works, they are typically performed via linear operations such as concatenation
and min pooling. We show that more compact and distinctive representations can
be achieved by optimizing a neural network (NN) model under the triplet
framework that non-linearly fuses local geometric features in Euclidean spaces.
The NN model is trained by an improved triplet loss function that fully
leverages all pairwise relationships within the triplet. Moreover, the fused
descriptor by our approach is also competitive to deep learned descriptors from
raw data while being more lightweight and rotational invariant. Experimental
results on four standard datasets with various data modalities and application
contexts confirm the advantages of our approach in terms of both feature
matching and geometric registration
Automatic and Precise Orthorectification, Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements
We describe a procedure to accurately measure ground deformations from optical satellite images. Precise orthorectification is obtained owing to an optimized model of the imaging system, where look directions are linearly corrected to compensate for attitude drifts, and sensor orientation uncertainties are accounted for. We introduce a new computation of the inverse projection matrices for which a rigorous resampling is proposed. The irregular resampling problem is explicitly addressed to avoid introducing aliasing in the ortho-rectified images. Image registration and correlation is achieved with a new iterative unbiased processor that estimates the phase plane in the Fourier domain for subpixel shift detection. Without using supplementary data, raw images are wrapped onto the digital elevation model and coregistered with a 1/50 pixel accuracy. The procedure applies to images from any pushbroom imaging system. We analyze its performance using Satellite pour l'Observation de la Terre (SPOT) images in the case of a null test (no coseismic deformation) and in the case of large coseismic deformations due to the Mw 7.1 Hector Mine, California, earthquake of 1999. The proposed technique would also allow precise coregistration of images for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection applications. A complete software package, the Coregistration of Optically Sensed Images and Correlation, is available for download from the Caltech Tectonics Observatory website
Automatic creation of urban velocity fields from aerial video
In this paper, we present a system for modelling vehicle motion in an urban
scene from low frame-rate aerial video. In particular, the scene is modelled as
a probability distribution over velocities at every pixel in the image.
We describe the complete system for acquiring this model. The video is
captured from a helicopter and stabilized by warping the images to match an
orthorectified image of the area. A pixel classifier is applied to the
stabilized images, and the response is segmented to determine car locations and
orientations. The results are fed in to a tracking scheme which tracks cars for
three frames, creating tracklets. This allows the tracker to use a combination
of velocity, direction, appearance, and acceleration cues to keep only tracks
likely to be correct. Each tracklet provides a measurement of the car velocity
at every point along the tracklet's length, and these are then aggregated to
create a histogram of vehicle velocities at every pixel in the image.
The results demonstrate that the velocity probability distribution prior can
be used to infer a variety of information about road lane directions, speed
limits, vehicle speeds and common trajectories, and traffic bottlenecks, as
well as providing a means of describing environmental knowledge about traffic
rules that can be used in tracking.Comment: 8 pages, 5 figure
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