1,734 research outputs found
A Locally Adaptable Iterative RX Detector
We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better first- and second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable Receiver Operating Characteristic (ROC) curves when compared to a global anomaly detector based upon the Support Vector Data Description (SVDD) algorithm, the conventional RX detector, and decomposed versions of the LAIRX detector. Furthermore, the utilization of parallel and distributed processing allows fast processing time making LAIRX applicable in an operational setting
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
Robust Linear Spectral Unmixing using Anomaly Detection
This paper presents a Bayesian algorithm for linear spectral unmixing of
hyperspectral images that accounts for anomalies present in the data. The model
proposed assumes that the pixel reflectances are linear mixtures of unknown
endmembers, corrupted by an additional nonlinear term modelling anomalies and
additive Gaussian noise. A Markov random field is used for anomaly detection
based on the spatial and spectral structures of the anomalies. This allows
outliers to be identified in particular regions and wavelengths of the data
cube. A Bayesian algorithm is proposed to estimate the parameters involved in
the model yielding a joint linear unmixing and anomaly detection algorithm.
Simulations conducted with synthetic and real hyperspectral images demonstrate
the accuracy of the proposed unmixing and outlier detection strategy for the
analysis of hyperspectral images
Quality criteria benchmark for hyperspectral imagery
Hyperspectral data appear to be of a growing interest
over the past few years. However, applications for hyperspectral
data are still in their infancy as handling the significant size of
the data presents a challenge for the user community. Efficient
compression techniques are required, and lossy compression,
specifically, will have a role to play, provided its impact on remote
sensing applications remains insignificant. To assess the data
quality, suitable distortion measures relevant to end-user applications
are required. Quality criteria are also of a major interest
for the conception and development of new sensors to define their
requirements and specifications. This paper proposes a method to
evaluate quality criteria in the context of hyperspectral images.
The purpose is to provide quality criteria relevant to the impact
of degradations on several classification applications. Different
quality criteria are considered. Some are traditionnally used in
image and video coding and are adapted here to hyperspectral
images. Others are specific to hyperspectral data.We also propose
the adaptation of two advanced criteria in the presence of different
simulated degradations on AVIRIS hyperspectral images. Finally,
five criteria are selected to give an accurate representation of the
nature and the level of the degradation affecting hyperspectral
data
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Towards the Mitigation of Correlation Effects in the Analysis of Hyperspectral Imagery with Extension to Robust Parameter Design
Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of which are often ignored when detecting or classifying anomalies, are shown to improve algorithm performance. Global anomaly detectors require the user to provide various parameters to analyze an image. These user-defined settings can be thought of as control variables and certain properties of the imagery can be employed as noise variables. The presence of these separate factors suggests the use of Robust Parameter Design (RPD) to locate optimal settings for an algorithm. This research extends the standard RPD model to include three factor interactions. These new models are then applied to the Autonomous Global Anomaly Detector (AutoGAD) to demonstrate improved setting combinations
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