Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling

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Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.

This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to inference with synthetic aperture radars (SAR). Sparse scenarios are considered where the interesting element is embedded in a much larger signal space. Policies are examined that adaptively distribute the constrained resources by using observed measurements to inform the allocation at subsequent stages. This thesis studies adaptive allocation policies in three main directions. First, a framework for adaptive search for sparse targets is proposed to simultaneously detect and track moving targets. Previous work is extended to include a dynamic target model that incorporates target transitions, birth/death probabilities, and varying target amplitudes. Policies are proposed that are shown empirically to have excellent asymptotic performance in estimation error, detection probability, and robustness to model mismatch. Moreover, policies are provided with low computational complexity as compared to state-of-the-art dynamic programming solutions. Second, adaptive sensor management is studied for stable tracking of targets under different modalities. A sensor scheduling policy is proposed that guarantees that the target spatial uncertainty remains bounded. When stability conditions are met, fundamental performance limits are derived such as the maximum number of targets that can be tracked stably and the maximum spatial uncertainty of those targets. The theory is extended to the case where the system may be engaged in tasks other than tracking, such as wide area search or target classification. Lastly, these developed tools are applied to tracking targets using SAR imagery. A hierarchical Bayesian model is proposed for efficient estimation of the posterior distribution for the target and clutter states given observed SAR imagery. This model provides a unifying framework that models the physical, kinematic, and statistical properties of SAR imagery. It is shown that this method generally outperforms common algorithms for change detection. Moreover, the proposed method has the additional benefits of (a) easily incorporating additional information such as target motion models and/or correlated measurements, (b) having few tuning parameters, and (c) providing a characterization of the uncertainty in the state estimation process.PHDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97931/1/newstage_1.pd

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.Comment: 30 page

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1