Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods

Rock type discrimination using Landsat-8 OLI satellite data in mafic-ultramafic terrain

The mafic-ultramafic terrain of the Bhavani complex in southern India is considered for lithological mapping. The Landsat-8 OLI satellite data was used for the interpretation of different rock types in the study area. The satellite data were digitally processed using ENVI 5.6 image processing software. In the OLI data, excluding bands 8 and 9, the remaining seven bands were used for the generation of colour composite images, band ratios, principal component analysis and SVM classification. Reflectance spectral measurements were carried out in laboratory conditions for five rock samples collected from the study area. The XRF analysis was carried out to estimate the composition of major oxides present in the rock samples. The results obtained from XRF analysis were compared with the rock spectra in characterizing the spectral features of the rock types. The colour composite images (B543, B567, B456, and B457), PCA composite image (PC312 and PC456), band ratios (BR5/5 and BR4/3), colour composite images from band ratios, and SVM classified output are useful in delineation various rock types in the terrain.  &nbsp

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Multispectral Imaging For Face Recognition Over Varying Illumination

This dissertation addresses the advantage of using multispectral narrow-band images over conventional broad-band images for improved face recognition under varying illumination. To verify the effectiveness of multispectral images for improving face recognition performance, three sequential procedures are taken into action: multispectral face image acquisition, image fusion for multispectral and spectral band selection to remove information redundancy. Several efficient image fusion algorithms are proposed and conducted on spectral narrow-band face images in comparison to conventional images. Physics-based weighted fusion and illumination adjustment fusion make good use of spectral information in multispectral imaging process. The results demonstrate that fused narrow-band images outperform the conventional broad-band images under varying illuminations. In the case where multispectral images are acquired over severe changes in daylight, the fused images outperform conventional broad-band images by up to 78%. The success of fusing multispectral images lies in the fact that multispectral images can separate the illumination information from the reflectance of objects which is impossible for conventional broad-band images. To reduce the information redundancy among multispectral images and simplify the imaging system, distance-based band selection is proposed where a quantitative evaluation metric is defined to evaluate and differentiate the performance of multispectral narrow-band images. This method is proved to be exceptionally robust to parameter changes. Furthermore, complexity-guided distance-based band selection is proposed using model selection criterion for an automatic selection. The performance of selected bands outperforms the conventional images by up to 15%. From the significant performance improvement via distance-based band selection and complexity-guided distance-based band selection, we prove that specific facial information carried in certain narrow-band spectral images can enhance face recognition performance compared to broad-band images. In addition, both algorithms are proved to be independent to recognition engines. Significant performance improvement is achieved by proposed image fusion and band selection algorithms under varying illumination including outdoor daylight conditions. Our proposed imaging system and image processing algorithms lead to a new avenue of automatic face recognition system towards a better recognition performance than the conventional peer system over varying illuminations

Multispectral imaging for preclinical assessment of rheumatoid arthritis models

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune condition affecting multiple body systems. Murine models of RA are vital in progressing understanding of the disease. The severity of arthritis symptoms is currently assessed in vivo by observations and subjective scoring which are time-consuming and prone to bias and inaccuracy. The main aim of this thesis is to determine whether multispectral imaging of murine arthritis models has the potential to assess the severity of arthritis symptoms in vivo in an objective manner. Given that pathology can influence the optical properties of a tissue, changes may be detectable in the spectral response. Monte Carlo modelling of reflectance and transmittance for varying levels of blood volume fraction, blood oxygen saturation, and water percentage in the mouse paw tissue demonstrated spectral changes consistent with the reported/published physiological markers of arthritis. Subsequent reflectance and transmittance in vivo spectroscopy of the hind paw successfully detected significant spectral differences between normal and arthritic mice. Using a novel non-contact imaging system, multispectral reflectance and transmittance images were simultaneously collected, enabling investigation of arthritis symptoms at different anatomical paw locations. In a blind experiment, Principal Component (PC) analysis of four regions of the paw was successful in identifying all 6 arthritic mice in a total sample of 10. The first PC scores for the TNF dARE arthritis model were found to correlate significantly with bone erosion ratio results from microCT, histology scoring, and the manual scoring method. In a longitudinal study at 5, 7 and 9 weeks the PC scores identified changes in spectral responses at an early stage in arthritis development for the TNF dARE model, before clinical signs were manifest. Comparison of the multispectral image data with the Monte Carlo simulations suggest that in this study decreased oxygen saturation is likely to be the most significant factor differentiating arthritic mice from their normal littermates. The results of the experiments are indicative that multispectral imaging performs well as an assessor of arthritis for RA models and may outperform existing techniques. This has implications for better assessment of preclinical arthritis and hence for better experimental outcomes and improvement of animal welfare

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Agroforestry as a post-mining land-use approach for waste deposits in alluvial gold mining areas of Colombia

Alluvial gold mining generates a vast amount of extractive waste that completely covers the natural soil, destroys riparian ecosystems, and negatively impacts river beds and valleys. Since 2002, a gold mining company has striven to create agroforestry plots in the waste deposits as a post-mining management approach, where agricultural crops and livestock are combined to complement reforestation in the area. This research aims at supporting reclamation of waste deposits by providing a comprehensive understanding of processes to manage the transition of nutrient-poor and acidic deposition sites towards productive agroforestry-based systems. Major components of this research comprise (i) an analysis of environmental and social challenges of the gold mining sector in Colombia, and its potential opportunities to add value to affected communities, (ii) an assessment of management practices and decision-making processes of the farmers working on reclamation areas, (iii) an analysis of the sources of variability of waste deposits from the perspective of soil development and vegetation succession, (iv) an analysis of spatial variability of the physicochemical properties of waste deposits with a spatially explicit management scheme, and (v) an assessment of vegetation recovery in terms of biomass and plant community composition. Farmers who are currently working on areas undergoing reclamation rely mostly on their own local knowledge to respond to the challenges that the heavily disturbed conditions of the area pose to crop establishment. Therefore, increasing their awareness of the inherent heterogeneity of their fields, as well as the interdependencies between management practices and improvement of soil fertility, may increase the productivity of their farms. The analysis of sources of variability of the waste deposits generated by alluvial gold mining revealed that these deposits are primarily influenced by the parent material of the alluvial gold deposits and by the technology used for gold mining (bucket or suction dredges), which define the type of deposit formed (gravel or sand). Waste deposits can provide essential functions for rural areas such as woody biomass production and crop establishment if deposits are managed according to a specific purpose, and crop selection for each deposit is done based on physicochemical and structural soil properties. This finding is echoed by the spatial assessment of vegetation reestablishment through the combination of remote sensing with machine-learning techniques that show a high spatial variability of textural properties and nutrient contents of the deposits. A management approach is proposed with the use of delineated management zones, which can lead to an overall increased productivity by developing strategies suitable to the characteristics of each field and its potential uses.Agroforstwirtschaft als Landnutzungsansatz auf Abraumdeponien in alluvialen Goldabbaugebieten Kolumbiens Der Abbau von alluvialem Gold erzeugt eine große Menge mineralischen Abfalls, der den natürlichen Boden vollständig bedeckt, Uferökosysteme zerstört, und Flussbetten und -täler negativ beeinflusst. Von einem Goldminenbetreiber werden seit 2002, als ein Ansatz einer Postbergbaustrategie, Agroforstparzellen in Abraumdeponien angelegt. In diesen werden landwirtschaftliche Nutzpflanzen und Viehhaltung zur Aufforstung der Parzelle kombiniert eingesetzt. Diese Forschungsarbeit beabsichtigt die Rekultivierungsmaßnahmen in Agroforstparzellen durch ein umfassendes Verständnis der beteiligten Prozesse zu unterstützen und den Übergang von nährstoffarmen und sauren Abraumdeponien hin zu produktiven agroforstbasierten Systemen zu steuern. Die Hauptbestandteile dieser Arbeit umfassen (i) eine Analyse der ökologischen und sozialen Herausforderungen des Goldminensektors in Kolumbien und potenzielle Möglichkeiten einen Mehrwert für die betroffenen Gemeinden zu schaffen, (ii) eine Bewertung der Managementpraktiken und Entscheidungsprozesse der Landwirte im Rahmen der Rückgewinnung von Landnutzungsflächen, (iii) eine Analyse der Ursachen von Varianz zwischen Abfalldeponien aus der Perspektive der Boden- und Vegetationsentwicklung, (iv) eine Analyse der räumlichen Variabilität der physikochemischen Eigenschaften von mineralischen Abraumdeponien mit einem räumlich expliziten Managementschema und (v) eine Bewertung der Vegetationserholung im Sinne der Zusammensetzung von Biomasse und Pflanzengemeinschaften. Landwirte die in Gebieten arbeiten die gegenwärtig einer Rekultivierung unterzogen werden, verlassen sich größtenteils auf ihre lokalen Erfahrungswerte, um mit den Herausforderungen für die Nutzpflanzenproduktion umzugehen, die durch die stark gestörten Bodenbedingungen verursacht werden. Eine Steigerung des Bewusstseins der lokalen Farmer für die inhärente Heterogenität ihrer Felder, sowie der Interdependenzen zwischen Managementpraktiken und der Verbesserung der Bodenfruchtbarkeit, kann die Produktivität der Farmbetriebe erhöhen. Die Analyse der Variabilitätsquellen der durch den alluvialen Goldabbau entstandenen mineralischen Abfalllager ergab, dass diese Lagerstätten in erster Linie vom Grundgestein der alluvialen Goldlagerstätten und der verwendeten Abbautechnik (Schaufel- oder Saugbagger) beeinflusst werden. Diese Faktoren bestimmen die Art der gebildeten Ablagerung (Kies oder Sand). Abfalldeponien können wesentliche Funktionen für ländliche Gebiete wie die Produktion von Holzbiomasse und den Anbau von Nutzpflanzen ermöglichen, wenn die Lagerstätten einem bestimmten Zweck entsprechend bewirtschaftet werden und die Auswahl der Kulturen für jede Lagerstätte auf Grundlage der spezifischen physikochemischen und strukturellen Bodeneigenschaften erfolgt. Dieser Befund wird durch die räumliche Bewertung der Vegetationsneubildung durch die Kombination von Fernerkundung mit maschinellen Lerntechniken bestätigt, die eine hohe räumliche Variabilität der Textureigenschaften und Nährstoffgehalte der Deponien zeigt. Es wird ein Managementansatz vorgeschlagen, bei dem abgegrenzte Bewirtschaftungszonen unterteilt werden. Dies kann zu einer insgesamt höheren Produktivität führen, indem Strategien entwickelt werden, die den Eigenschaften jedes einzelnen Feldes und seiner potenziellen Nutzungsmöglichkeiten entsprechen

The evaluation of Corona and Ikonos satellite imagery for archaeological applications in a semi-arid environment

Archaeologists have been aware of the potential of satellite imagery as a tool almost since the first Earth remote sensing satellite. Initially sensors such as Landsat had a ground resolution which was too coarse for thorough archaeological prospection although the imagery was used for geo-archaeological and enviro-archaeological analyses. In the intervening years the spatial and spectral resolution of these sensing devices has improved. In recent years two important occurrences enhanced the archaeological applicability of imagery from satellite platforms: The declassification of high resolution photography by the American and Russian governments and the deregulation of commercial remote sensing systems allowing the collection of sub metre resolution imagery. This thesis aims to evaluate the archaeological application of three potentially important resources; Corona space photography and Ikonos panchromatic and multispectral imager). These resources are evaluated in conjunction with Landsat Thematic Mapper (TM) imagery over a 600 square km study area in the semi-arid environment around Homs, Syria. The archaeological resource in this area is poorly understood, mapped and documented. The images are evaluated for their ability to create thematic layers and to locate archaeological residues in different environmental zones. Further consideration is given to the physical factors that allow archaeological residues to be identified and how satellite imagery and modern technology may impact on Cultural Resource Management. This research demonstrates that modern high resolution and historic satellite imagery can be important tools for archaeologists studying in semi-arid environments. The imagery has allowed a representative range of archaeological features and landscape themes to be identified. The research shows that the use of satellite imagery can have significant impact on the design of the archaeological survey in the middle-east and perhaps in other environments