Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Sparse representation based hyperspectral image compression and classification

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

Textural and Rule-based Lithological Classification of Remote Sensing Data, and Geological Mapping in Southwestern Prieska Sub-basin, Transvaal Supergroup, South Africa

Although remote sensing has been widely used in geological investigations, the lithological classification of the area interested, based on medium-spatial and spectral resolution satellite data, is often not successful because of the complicated geological situation and other factors like inadequate methodology applied and wrong geological models. The study area of the present thesis is located in southwest of the Prieska sub-basin, Transvaal Supergroup, South Africa. This area includes mainly Neoarchean and Proterozoic sedimentary rocks partly uncomfortably covered by uppermost Paleozoic and lower Mesozoic rocks and Tertiary to recent soils and sands. The Precambrian rocks include various formations of volcanic and intrusive rocks, quartzites, shales, platform carbonates and Banded Iron Formations (BIF). The younger rocks and soils include dikes and shales, glacial sedimentary rocks, coarser siliciclastic rocks, calcretes, aeolian and fluvial sands, etc. Prospect activity for mineral deposits necessitates the detailed geological map (1:100000) of the area. In this research, a new rule-based classification system (RBS) was put forward, integrating spectral characteristics, textural features and ancillary data, such as general geological map (1:250000) and elevation data, in order to improve the lithological classification accuracy and the subsequent mapping accuracy in the study area. The proposed technique was mainly based on Landsat TM data and ASTER data with medium resolution. As ancillary data sets, topographic maps and general geological map were also available. Software like ERDAS©, Matlab©, and ArcGIS© supported the procedures of classification and mapping. The newly developed classification technique was performed by three steps. Firstly, the geographic and atmospheric correction was performed on the original TM and ASTER data, following the principal component analysis (PCA) and band ratioing, to enhance the images and to obtain data sets like principal components (PCs) and ratio bands. Traditional maximum-likelihood supervised classification (MLC) was performed individually on enhanced multispectral image and principal components image (PCs-image). For TM data, the classification accuracy based on PCs-image was higher than that based on multispectral image. For ASTER data, the classification accuracy of PCs- image was close to but lower, than that of multispectral image. As one of the encountered Banded Iron Formations, the Griquatown Banded Iron Formation (G-BIF) was recognized well in TM-principal components image (PCs-image). In the second step, textural features of different lithological types based on TM data were analyzed. Grey level co-occurrence matrix (GLCM) based textural features were computed individually from band 5 and the first principal component (PC1) of TM data. Geostatistics-based textural features were computed individually from the 6 TM multispectral bands and 3 principal components (PC1, PC2 and PC3). These two kinds of textural features were individually stacked as extra layers together with the original 6 multispectral bands and the 6 principal components to form several new data sets. Ratio bands were also individually stacked as extra layers with 6 multispectral bands and 6 principal components, to form other new data sets. In the same way new data sets were formed based on ASTER data. Then, all of the new data sets were individually classified using a maximum likelihood supervised classification (MLC), to produce several classified thematic images. The classification accuracy based on the new data sets are higher than that solely based on the spectral characteristics of original TM and ASTER data. It should be noticed that for one specific rock type, the class value in all classified images should correspond to its identified (ID) value in digital geological map. The third step was to perform the rule-based system (RBS) classification. In the first part of the RBS, two classified images were analyzed and compared. The analysis was based on the classification results in the first step, and the elevation data detracted from the topographic map. In comparison, the pixels with high possibility of being classified correctly (consistent pixels) and the pixels with high possibility of being misclassified (inconsistent pixels) were separately marked. In the second part of the RBS, the class values of consistent pixels were kept unchanged, and the class values of inconsistent pixels were replaced by their values in digital geological map (1:250000). Compared to the results solely based on spectral characteristics of TM data (54.3%) and ASTER data (66.41%), the new RBS classification improved the accuracy (83.2%) significantly. Based on the classification results, the detailed lithological map (1:100000) of the study area was edited. Photo-lineaments were interpreted from multi data source (MDS), including enhanced satellite images, slope images, shaded relief images and drainage maps. The interpreted lineaments were compared to those, digitized from general geological map and followed by a simple lineament analysis compared to published literatures. The results show the individual merits of lineament detection from MDS and general geological map. A final lineament map (1:100000) was obtained by integrating all the information. Ground check field work was carried out in 2009, to verify the classification and mapping, and the results were subsequently incorporated into the mapping and the classification procedures. Finally, a GIS-based detailed geological map (1:100000) of the study area was obtained, compiling the newly gained information from the performed classification and lineament analysis, from the field work and from published and available unpublished detailed geological maps. The here developed methods are proposed to be used for generation of new, detailed geological maps or updates of existent general geological maps by implementing the latest satellite images and all available ancillary data sets. Although final ground check field work is irreplaceable by remote sensing, the here presented research demonstrates the great potential and future prospects in lithological classification and geological mapping, for mineral exploration

On-line quality control in polymer processing using hyperspectral imaging

L’industrie du plastique se tourne de plus en plus vers les matériaux composites afin d’économiser de la matière et/ou d’utiliser des matières premières à moindres coûts, tout en conservant de bonnes propriétés. L’impressionnante adaptabilité des matériaux composites provient du fait que le manufacturier peut modifier le choix des matériaux utilisés, la proportion selon laquelle ils sont mélangés, ainsi que la méthode de mise en œuvre utilisée. La principale difficulté associée au développement de ces matériaux est l’hétérogénéité de composition ou de structure, qui entraîne généralement des défaillances mécaniques. La qualité des prototypes est normalement mesurée en laboratoire, à partir de tests destructifs et de méthodes nécessitant la préparation des échantillons. La mesure en-ligne de la qualité permettrait une rétroaction quasi-immédiate sur les conditions d’opération des équipements, en plus d’être directement utilisable pour le contrôle de la qualité dans une situation de production industrielle. L’objectif de la recherche proposée consiste à développer un outil de contrôle de qualité pour la qualité des matériaux plastiques de tout genre. Quelques sondes de type proche infrarouge ou ultrasons existent présentement pour la mesure de la composition en-ligne, mais celles-ci ne fournissent qu’une valeur ponctuelle à chaque acquisition. Ce type de méthode est donc mal adapté pour identifier la distribution des caractéristiques de surface de la pièce (i.e. homogénéité, orientation, dispersion). Afin d’atteindre cet objectif, un système d’imagerie hyperspectrale est proposé. À l’aide de cet appareil, il est possible de balayer la surface de la pièce et d’obtenir une image hyperspectrale, c’est-à-dire une image formée de l’intensité lumineuse à des centaines de longueurs d’onde et ce, pour chaque pixel de l’image. L’application de méthodes chimiométriques permettent ensuite d’extraire les caractéristiques spatiales et spectrales de l’échantillon présentes dans ces images. Finalement, les méthodes de régression multivariée permettent d’établir un modèle liant les caractéristiques identifiées aux propriétés de la pièce. La construction d’un modèle mathématique forme donc l’outil d’analyse en-ligne de la qualité des pièces qui peut également prédire et optimiser les conditions de fabrication.The use of plastic composite materials has been increasing in recent years in order to reduce the amount of material used and/or use more economic materials, all of which without compromising the properties. The impressive adaptability of these composite materials comes from the fact that the manufacturer can choose the raw materials, the proportion in which they are blended as well as the processing conditions. However, these materials tend to suffer from heterogeneous compositions and structures, which lead to mechanical weaknesses. Product quality is generally measured in the laboratory, using destructive tests often requiring extensive sample preparation. On-line quality control would allow near-immediate feedback on the operating conditions and may be transferrable to an industrial production context. The proposed research consists of developing an on-line quality control tool adaptable to plastic materials of all types. A number of infrared and ultrasound probes presently exist for on-line composition estimation, but only provide single-point values at each acquisition. These methods are therefore less adapted for identifying the spatial distribution of a sample’s surface characteristics (e.g. homogeneity, orientation, dispersion). In order to achieve this objective, a hyperspectral imaging system is proposed. Using this tool, it is possible to scan the surface of a sample and obtain a hyperspectral image, that is to say an image in which each pixel captures the light intensity at hundreds of wavelengths. Chemometrics methods can then be applied to this image in order to extract the relevant spatial and spectral features. Finally, multivariate regression methods are used to build a model between these features and the properties of the sample. This mathematical model forms the backbone of an on-line quality assessment tool used to predict and optimize the operating conditions under which the samples are processed

VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNN), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterise the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery, and partition this uncertainty into positive regions (correct classifications) and non-positive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a Multi-Layer Perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as Semantic Labelling datasets. The MRF-CNN consistently outperformed the benchmark MLP, SVM, MLP-MRF and CNN and the baseline methods. This research provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification

Mixture of Latent Variable Models for Remotely Sensed Image Processing

The processing of remotely sensed data is innately an inverse problem where properties of spatial processes are inferred from the observations based on a generative model. Meaningful data inversion relies on well-defined generative models that capture key factors in the relationship between the underlying physical process and the measurements. Unfortunately, as two mainstream data processing techniques, both mixture models and latent variables models (LVM) are inadequate in describing the complex relationship between the spatial process and the remote sensing data. Consequently, mixture models, such as K-Means, Gaussian Mixture Model (GMM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), characterize a class by statistics in the original space, ignoring the fact that a class can be better represented by discriminative signals in the hidden/latent feature space, while LVMs, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Sparse Representation (SR), seek representational signals in the whole image scene that involves multiple spatial processes, neglecting the fact that signal discovery for individual processes is more efficient. Although the combined use of mixture model and LVMs is required for remote sensing data analysis, there is still a lack of systematic exploration on this important topic in remote sensing literature. Driven by the above considerations, this thesis therefore introduces a mixture of LVM (MLVM) framework for combining the mixture models and LVMs, under which three models are developed in order to address different aspects of remote sensing data processing: (1) a mixture of probabilistic SR (MPSR) is proposed for supervised classification of hyperspectral remote sensing imagery, considering that SR is an emerging and powerful technique for feature extraction and data representation; (2) a mixture model of K “Purified” means (K-P-Means) is proposed for addressing the spectral endmember estimation, which is a fundamental issue in remote sensing data analysis; (3) and a clustering-based PCA model is introduced for SAR image denoising. Under a unified optimization scheme, all models are solved via Expectation and Maximization (EM) algorithm, by iteratively estimating the two groups of parameters, i.e., the labels of pixels and the latent variables. Experiments on simulated data and real remote sensing data demonstrate the advantages of the proposed models in the respective applications

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere