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

A probablistic framework for classification and fusion of remotely sensed hyperspectral data

Reliable and accurate material identification is a crucial component underlying higher-level autonomous tasks within the context of autonomous mining. Such tasks can include exploration, reconnaissance and guidance of machines (e.g. autonomous diggers and haul trucks) to mine sites. This thesis focuses on the problem of classification of materials (rocks and minerals) using high spatial and high spectral resolution (hyperspectral) imagery, collected remotely from mine faces in operational open pit mines. A new method is developed for the classification of hyperspectral data including field spectra and imagery using a probabilistic framework and Gaussian Process regression. The developed method uses, for the first time, the Observation Angle Dependent (OAD) covariance function to classify high-dimensional sets of data. The performance of the proposed method of classification is assessed and compared to standard methods used for the classification of hyperspectral data. This is done using a staged experimental framework. First, the proposed method is tested using high-resolution field spectrometer data acquired in the laboratory and in the field. Second, the method is extended to work on hyperspectral imagery acquired in the laboratory and its performance evaluated. Finally, the method is evaluated for imagery acquired from a mine face under natural illumination and the use of independent spectral libraries to classify imagery is explored. A probabilistic framework was selected because it best enables the integration of internal and external information from a variety of sensors. To demonstrate advantages of the proposed GP-OAD method over existing, deterministic methods, a new framework is proposed to fuse hyperspectral images using the classified probabilistic outputs from several different images acquired of the same mine face. This method maximises the amount of information but reduces the amount of data by condensing all available information into a single map. Thus, the proposed fusion framework removes the need to manually select a single classification among many individual classifications of a mine face as the `best' one and increases the classification performance by combining more information. The methods proposed in this thesis are steps forward towards an automated mine face inspection system that can be used within the existing autonomous mining framework to improve productivity and efficiency. Last but not least the proposed methods will also contribute to increased mine safety

Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery

This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using an endmember extraction algorithm such as the famous N-finder (N-FINDR) or Vertex Component Analysis (VCA) algorithms. This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas conjugate priors are chosen for the remaining parameters. A hybrid Gibbs sampler is then constructed to generate abundance and variance samples distributed according to the joint posterior of the abundances and noise variances. The performance of the proposed methodology is evaluated by comparison with other unmixing algorithms on synthetic and real images

Short wave infrared chemical imaging as future tool for analysing gunshot residues patterns in targets

This work used chemical imaging in the short-wave infrared region for analysing gunshot residues (GSR) patterns in cotton fabric targets shot with conventional and non-toxic ammunition. It presents a non-destructive, non-toxic, highly visual and hiperspectral-based approach. The method was based on classical least squares regression, and was tested with the ammunition propellants and their standard components' spectra. The propellants' spectra were satisfactorily used (R2 >0.966, and CorrCoef >0.982) for identifying the GSR irrespective of the type of ammunition used for the shooting. In a more versatile approach, nitrocellulose, the main component in the ammunition propellants, resulted an excellent standard for identifying GSR patterns (R2>0.842, and CorrCoef >0.908). In this case, the propellants' stabilizers (diphenilamine and centralite), and its nitrated derivatives as well as dinitrotoluene, showed also high spectral activity. Therefore, they could be recommended as complementary standards for confirming the GSR identification. These findings establish the proof of concept for a science-based evidence useful to support expert reports and final court rulings. This approach for obtaining GSR patterns can be an excellent alternative to the current and traditional chemical methods, which are based in presumptive and invasive colour tests.Ministerio de Economía y Competitividad (MINECO

Deep Learning for Remote Sensing Image Processing

Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth\u27s surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have been investigated and customized for satellite image processing in the applications of landcover classification and ground object detection. First, a simple and effective Convolutional Neural Network (CNN) model is developed to detect fresh soil from tunnel digging activities near the U.S. and Mexico border by using pansharpened synthetic hyperspectral images. These tunnels’ exits are usually hidden under warehouses and are used for illegal activities, for example, by drug dealers. Detecting fresh soil nearby is an indirect way to search for these tunnels. While multispectral images have been used widely and regularly in remote sensing since the 1970s, with the fast advances in hyperspectral sensors, hyperspectral imagery is becoming popular. A combination of 80 synthetic hyperspectral channels with the original eight multispectral channels collected by the WorldView-2 satellite are used by CNN to detect fresh soil. Experimental results show that detection performance can be significantly improved by the combination of synthetic hyperspectral images with those original multispectral channels. Second, an end-to-end, pixel-level Fully Convolutional Network (FCN) model is implemented to estimate the number of refugee tents in the Rukban area near the Syrian-Jordan border using high-resolution multispectral satellite images collected by WordView-2. Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees have fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor in maintaining the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. The FCN model is also boosted by transfer learning with the knowledge in the pre-trained VGG-16 model. Experimental results show that the FCN model is very accurate and has less than 2% of error. Last, we investigate the Generative Adversarial Networks (GAN) to augment training data to improve the training of FCN model for refugee tent detection. Segmentation based methods like FCN require a large amount of finely labeled images for training. In practice, this is labor-intensive, time consuming, and tedious. The data-hungry problem is currently a big hurdle for this application. Experimental results show that the GAN model is a better tool as compared to traditional methods for data augmentation. Overall, our research made a significant contribution to remote sensing image processin

Face Recognition via Ensemble Sift Matching of Uncorrelated Hyperspectral Bands and Spectral PCTS

Face recognition is not a new area of study, but facial recognition using through hyperspectral images is a somewhat new concept which is still in its infancy. Although the conventional method of face recognition using Red-Green-Blue (RGB) or grayscale images has been advanced over the last twenty years, these methods are still shown to have weak performance whenever there are variations or changes in lighting, pose, or temporal aspect of the subjects. A hyperspectral representation of an image captures more information that is available within a scene than a RGB image therefore it is beneficial to study the performance of face recognition using a hyperspectral representation of the subjects\u27 faces. We studied the results of a variety of methods for performing face recognition using the Scale Invariant Transformation Feature (SIFT) algorithm as a matching function on uncorrelated spectral bands, principal component representation of the spectral bands, and the ensemble decision of the two. We conclude that there is no dominating method in the scope of our research; however, we do obtain three methods with leading performances despite some trade-off between performance at lower ranks and performance at higher ranks...that outperform the results obtained from a previous study which only considered a SIFT application on a single hyperspectral band which also performs very well under temporal variation