1,594 research outputs found
Techniques for automatic large scale change analysis of temporal multispectral imagery
Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst\u27s job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change
Autonomous Coastal Land Cover Assessment Using Polarimetric Decomposition of SAR Data
The paper reports an experiment on classification using fully polarimetric SAR data. Many reports have been presented mentioning test sites in temperate regions utilizing polarimetric SAR data from airborne and/or spaceborne SAR sensors. However, few studies are dedicated to tropical region which highly dynamic land uses are observed. Using the AirSAR Sungai Wain fully polarimetric data, capability to extract features in coastal region has been demonstrated by an unsupervised classification technique fed by the CloudePottier decomposition theorem
Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering
We propose a new pattern recognition method that is able to reconstruct the
3D structure of the active part of a fault network using the spatial location
of earthquakes. The method is a generalization of the so-called dynamic
clustering method, that originally partitions a set of datapoints into
clusters, using a global minimization criterion over the spatial inertia of
those clusters. The new method improves on it by taking into account the full
spatial inertia tensor of each cluster, in order to partition the dataset into
fault-like, anisotropic clusters. Given a catalog of seismic events, the output
is the optimal set of plane segments that fits the spatial structure of the
data. Each plane segment is fully characterized by its location, size and
orientation. The main tunable parameter is the accuracy of the earthquake
localizations, which fixes the resolution, i.e. the residual variance of the
fit. The resolution determines the number of fault segments needed to describe
the earthquake catalog, the better the resolution, the finer the structure of
the reconstructed fault segments. The algorithm reconstructs successfully the
fault segments of synthetic earthquake catalogs. Applied to the real catalog
constituted of a subset of the aftershocks sequence of the 28th June 1992
Landers earthquake in Southern California, the reconstructed plane segments
fully agree with faults already known on geological maps, or with blind faults
that appear quite obvious on longer-term catalogs. Future improvements of the
method are discussed, as well as its potential use in the multi-scale study of
the inner structure of fault zones
Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.
This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications
Autonomous Coastal Land Cover Assessment Using Polarimetric Decomposition of SAR Data
The paper reports an experiment on classification using fully polarimetric SAR data. Many reports have been presented mentioning test sites in temperate regions utilizing polarimetric SAR data from airborne and/or spaceborne SAR sensors. However, few studies are dedicated to tropical region which highly dynamic land uses are observed. Using the AirSAR Sungai Wain fully polarimetric data, capability to extract features in coastal region has been demonstrated by an unsupervised classification technique fed by the CloudePottier decomposition theorem
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance
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This study aims to generalize color line to M-dimensional spectral line feature (M>3) and introduce methods for denoising and unmixing of hyperspectral images based on the spectral linearity.For denoising, we propose a local spectral component decomposition method based on the spectral line. We first calculate the spectral line of an M-channel image, then using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, thus the algorithm needs to denoise only two grayscale images, regardless of the number of channels. For unmixing, we propose an algorithm that exploits the low-rank local abundance by applying the unclear norm to the abundance matrix for local regions of spatial and abundance domains. In optimization problem, the local abundance regularizer is collaborated with the L2, 1 norm and the total variation.ćäčć·ćžç«ć€§
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