6 research outputs found

    Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

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    Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

    Sparse representation based hyperspectral image compression and classification

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    Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

    Analysis of Argonaute-Small RNA-Transcription Factor Circuits Controlling Leaf Development

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    Experimental studies of plant development have yielded many insights into gene regulation, revealing interactions between core transcriptional and post-transcriptional regulatory pathways present in all land plants. This work describes a direct connection between the three main small RNA-transcription factor circuits controlling leaf shape dynamics in the reference plant Arabidopsis thaliana. We used a high-throughput yeast 1-hybrid platform to identify factors directly binding the promoter of the highly specialized ARGONAUTE7 silencing factor. Two groups of developmentally significant microRNA-targeted transcription factors were the clearest hits from these screens, but transgenic complementation analysis indicated that their binding sites make only a small contribution to ARGONAUTE7 function, possibly indicating a role in fine tuning. Timelapse imaging methodology developed to quantify these small differences may have broad utility for plant biologists. Our analysis also clarified requirements for polar transcription of ARGONAUTE7. This work has implications for our understanding of patterning in land plants

    Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information

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    It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods

    University of Wollongong Undergraduate Handbook 2011

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