Principal Component Reconstruction Error for Hyperspectral Anomaly Detection

Jablonski, James; Bihl, Trevor J.; Bauer, Kenneth W.

doi:10.1109/lgrs.2015.2421813

Cited by 55 publications

(17 citation statements)

References 32 publications

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“…To assess the proposed method the following experiments were conducted: We first compared the proposed mitotic candidate identification phase with an alternative, principle component analysis (PCA) approach for detecting mitotic events as outliers, adapted from Jablonski et al (2015). Next, we demonstrated the clustering obtained based on the candidate daughters' symmetry and mother-daughters' dissimilarity ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

et al. 2018

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Motivation: Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques, and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence. Results: We present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters' similarity. Association is accomplished by defining cell neighborhood via a stochastic version of the Delaunay triangulation and optimization by dynamic programming. Our framework presents promising detection results for a variety of fluorescence microscopy datasets of different sources, including 2D and 3D sequences from the Cell Tracking Challenge. Availability: code is available in github:

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Section: Resultsmentioning

confidence: 99%

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

et al. 2018

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“…Reconstruction-based methods Reconstruction-based methods are another set of one-class classification approaches that characterize the typical class by learning a mapping between typical input examples and a lower-dimensional representation that minimizes the loss between the input and its reconstruction from the lower-dimensional representation. PCA can be used for reconstruction-based novelty detection, in which the reconstruction error between inputs and their inverse transformation from the principal subspace is used as a novelty score (e.g., Kwak 2008;Chandola et al 2009;Toivola et al 2010;Wagstaff et al 2013;Xiao et al 2013;Jablonski et al 2015). Diaz and Hollmen (2002) used kernel-based and least-squares based general regression neural networks (GRNNs) for novelty detection and showed that kernel based approaches provided more meaningful and interpretable residuals (reconstruction errors) than least squares approaches.…”

Section: Related Workmentioning

confidence: 99%

Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

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et al. 2020

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Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.

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“…Due to this reason, the impact of the compression-decompression process using just the HyperLCA transformation stage in the mentioned hyperspectral applications is evaluated and compared with the impact produced by the PCA transform for the same images and applications. It is worth to mention here that the PCA transform is used in many hyperspectral applications, such as unmixing or classification, for spectrally decorrelating the information and reducing the number of spectral components of the data set, with the goal of reducing the redundant information, increasing the separability of the different elements of interest and improving the application results [9,10,27]. Accordingly, the PCA transform can be considered as a good reference to compare with, regarding the impact of the HyperLCA transform in the ulterior hyperspectral applications.…”

Section: Evaluation Of the Impact Produced By The Hyperlca Compressiomentioning

confidence: 99%

A New Algorithm for the On-Board Compression of Hyperspectral Images

et al. 2018

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Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth's surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

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Principal Component Reconstruction Error for Hyperspectral Anomaly Detection

Cited by 55 publications

References 32 publications

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

A New Algorithm for the On-Board Compression of Hyperspectral Images

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