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Forest ecosystems have recently received worldwide attention due to their biological diversity and their major role in the global carbon balance. Detecting forest cover change is crucial for reporting forest status and assessing the evolution of forested areas. However, existing change detection approaches based on satellite remote sensing are not quite appropriate to rapidly process the large volume of earth observation data. Recent advances in image segmentation have led to new opportunities for a new object-based monitoring system. <br> <br> This thesis aims at developing and evaluating an automated object-based change detection method dedicated to high spatial resolution satellite images for identifying and mapping forest cover changes in different ecosystems. This research characterized the spectral reflectance dynamics of temperate forest stand cycle and found the use of several spectral bands better for the detection of forest cover changes than with any single band or vegetation index over different time periods. Combining multi-date image segmentation, image differencing and a dedicated statistical procedure of multivariate iterative trimming, an automated change detection algorithm was developed. This process has been further generalized in order to automatically derive an up-to-date forest mask and detect various deforestation patterns in tropical environment.<br> <br> Forest cover changes were detected with very high performances (>90 %) using 3 SPOT-HRVIR images over temperate forests. Furthermore, the overall results were better than for a pixel-based method. Overall accuracies ranging from 79 to 87% were achieved using SPOT-HRVIR and Landsat ETM imagery for identifying deforestation for two different case studies in the Virunga National Park (DRCongo). Last but not least, a new multi-scale mapping solution has been designed to represent change processes using spatially-explicit maps, i.e. deforestation rate maps. By successfully applying these complementary conceptual developments, a significant step has been done toward an operational system for monitoring forest in various ecosystems.

Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.

Laser based underwater triangulation ranging is sensitive to the environmental conditions and laser beam profile. Also, its ranging quality is greatly affected by the algorithm choices for peak detection and for image processing. By utilizing the merging least-squares approximation for laser image processing, it indeed succeeds in increasing quality of triangulation ranging in water; however, this result was obtained on the use of a laser beam with nearly circular cross-section. Therefore, by using an ellipse-like laser beam cross-section for range finding, we are really interested in understanding the quality of range finding with different peak detection algorithms. Besides, the ellipse orientation of the laser spot projected on the image plane would be various. We are also interested in learning about the relationship between the ellipse orientation and the quality of range finding. In this study, peak detection algorithms are investigated by considering four different laser beam cross-sections which are ircle, horizontal ellipse, oblique ellipse, and vertical ellipse. First, we employ polynomial regression for processing laser image to study the effect of polynomial degree on quality of triangulation ranging. It was found that the linear regression achieves the best ranging quality than others. Then, according to this result, the ranging quality associated with peak detection is evaluated by employing three different algorithms which are the illumination center, twice illumination center and the illumination center with principal component analysis. We found that the ranging quality by using the illumination center with principal component analysis is the best, next is twice illumination center, and last the illumination center. This result indicates that the orientation of elliptical laser beam has an influential effect on the quality of range finding. In addition, the ranging quality difference among peak detection algorithms is significantly reduced by implementing the merging least-squares approximation rlaser image processing. This result illustrates that the merging least-squares approximation does reduce the effect of peak detection algorithm on the quality of range finding.

Accurate, fast detection of clouds in satellite imagery has many applications, for example Numerical Weather Prediction (NWP) and climate studies of both the atmosphere and of the Earth’s surface temperature. Most operational techniques for cloud detection rely on the differences between observations of cloud and of clear-sky being more or less constant in space and in time. In reality, this is not the case - different clouds have different spectral properties, and different cloud types are more or less likely in different places and at different times, depending on atmospheric conditions and on the Earth’s surface properties. Observations of clear sky also vary in space and time, depending on atmospheric and surface conditions, and on the presence or absence of aerosol particles. The Bayesian approach adopted in this project allows pixel-specific physical information (for example from NWP) to be used to predict pixel-specific observations of clear sky. A physically-based, spatially- and temporally-specific probability that each pixel contains a cloud observation is then calculated. An advantage of this approach is that identification of ambiguously classed pixels from a probabilistic result is straightforward, in contrast to the binary result generally produced by operational techniques. This project has developed and validated the Bayesian approach to cloud detection, and has extended the range of applications for which it is suitable, achieving skills scores that match or exceed those achieved by operational methods in every case. High temperature gradients can make observations of clear sky around ocean fronts, particularly at thermal wavelengths, appear similar to cloud observations. To address this potential source of ambiguous cloud detection results, a region of imagery acquired by the AATSR sensor which was noted to contain some ocean fronts, was selected. Pixels in the region were clustered according to their spectral properties with the aim of separating pixels that correspond to different thermal regimes of the ocean. The mean spectral properties of pixels in each cluster were then processed using the Bayesian cloud detection technique and the resulting posterior probability of clear then assigned to individual pixels. Several clustering methods were investigated, and the most appropriate, which allowed pixels to be associated with multiple clusters, with a normalized vector of ‘membership strengths’, was used to conduct a case study. The distribution of final calculated probabilities of clear became markedly more bimodal when clustering was included, indicating fewer ambiguous classifications, but at the cost of some single pixel clouds being missed. While further investigations could provide a solution to this, the computational expense of the clustering method made this impractical to include in the work of this project. This new Bayesian approach to cloud detection has been successfully developed by this project to a point where it has been released under public license. Initially designed as a tool to aid retrieval of sea surface temperature from night-time imagery, this project has extended the Bayesian technique to be suitable for imagery acquired over land as well as sea, and for day-time as well as for night-time imagery. This was achieved using the land surface emissivity and surface reflectance parameter products available from the MODIS sensor. This project added a visible Radiative Transfer Model (RTM), developed at University of Edinburgh, and a kernel-based surface reflectance model, adapted here from that used by the MODIS sensor, to the cloud detection algorithm. In addition, the cloud detection algorithm was adapted to be more flexible, making its implementation for data from the SEVIRI sensor straightforward. A database of ‘difficult’ cloud and clear targets, in which a wide range of both spatial and temporal locations was represented, was provided by M´et´eo-France and used in this work to validate the extensions made to the cloud detection scheme and to compare the skill of the Bayesian approach with that of operational approaches. For night land and sea imagery, the Bayesian technique, with the improvements and extensions developed by this project, achieved skills scores 10% and 13% higher than M´et´eo-France respectively. For daytime sea imagery, the skills scores were within 1% of each other for both approaches, while for land imagery the Bayesian method achieved a 2% higher skills score. The main strength of the Bayesian technique is the physical basis of the differentiation between clear and cloud observations. Using NWP information to predict pixel-specific observations for clear-sky is relatively straightforward, but making such predictions for cloud observations is more complicated. The technique therefore relies on an empirical distribution rather than a pixel-specific prediction for cloud observations. To try and address this, this project developed a means of predicting cloudy observations through the fast forward-modelling of pixel-specific NWP information. All cloud fields in the pixel-specific NWP data were set to 0, and clouds were added to the profile at discrete intervals through the atmosphere, with cloud water- and ice- path (cwp, cip) also set to values spaced exponentially at discrete intervals up to saturation, and with cloud pixel fraction set to 25%, 50%, 75% and 100%. Only single-level, single-phase clouds were modelled, with the justification that the resulting distribution of predicted observations, once smoothed through considerations of uncertainties, is likely to include observations that would correspond to multi-phase and multi-level clouds. A fast RTM was run on the profile information for each of these individual clouds and cloud altitude-, cloud pixel fraction- and channel-specific relationships between cwp (and similarly cip) and predicted observations were calculated from the results of the RTM. These relationships were used to infer predicted observations for clouds with cwp/cip values other than those explicitly forward modelled. The parameters used to define the relationships were interpolated to define relationships for predicted observations of cloud at 10m vertical intervals through the atmosphere, with pixel coverage ranging from 25% to 100% in increments of 1%. A distribution of predicted cloud observations is then achieved without explicit forward-modelling of an impractical number of atmospheric states. Weights are applied to the representation of individual clouds within the final Probability Density Function (PDF) in order to make the distribution of predicted observations realistic, according to the pixel-specific NWP data, and to distributions seen in a global reference dataset of NWP profiles from the European Centre for Medium Range Weather Forecasting (ECMWF). The distribution is then convolved with uncertainties in forward-modelling, in the NWP data, and with sensor noise to create the final PDF in observation space, from which the conditional probability that the pixel observation corresponds to a cloud observation can be read. Although the relatively fast computational implementation of the technique was achieved, the results are disappointingly poor for the SEVIRI-acquired dataset, provided by M´et´eo-France, against which validation was carried out. This is thought to be explained by both the uncertainties in the NWP data, and the forward-modelling dependence on those uncertainties, being poorly understood, and treated too optimistically in the algorithm. Including more errors in the convolution introduces the problem of quantifying those errors (a non-trivial task), and would increase the processing time, making implementation impractical. In addition, if the uncertianties considered are too high then a PDF flatter than the empirical distribution currently used would be produced, making the technique less useful.

The School of Electrical, Electronic and Computer Engineering of the North-West University is conducting research about RFID (radio frequency identification) medium range reader systems for an international company, iPico. The focus area of the present research is the development of a robust tag detection algorithm for noisy environments. During the past three years a digital detection algorithm was developed. This digital detection algorithm delivered significant improvements in detection of RFIDs over its analogue counterpart, especially in noisy environments. However, the digital detection algorithm was found to be very sensitive with regard to data rate deviations. Although the latter algorithm improved the detection of RFIDs, ghost (absent) tags were now also detected. The objectives of this project are, to develop an enhanced detection algorithm which is less sensitive to frequency deviations and to eliminate the appearance of the so called ghost tags. The proposed enhanced algorithm will be implemented on a FPGA (field programmable gate array), more specific the Altera Cyclone EP1CT144C6 FPGA. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2009.

The School of Electrical, Electronic and Computer Engineering of the North-West University is conducting research about RFID (radio frequency identification) medium range reader systems for an international company, iPico. The focus area of the present research is the development of a robust tag detection algorithm for noisy environments. During the past three years a digital detection algorithm was developed. This digital detection algorithm delivered significant improvements in detection of RFIDs over its analogue counterpart, especially in noisy environments. However, the digital detection algorithm was found to be very sensitive with regard to data rate deviations. Although the latter algorithm improved the detection of RFIDs, ghost (absent) tags were now also detected. The objectives of this project are, to develop an enhanced detection algorithm which is less sensitive to frequency deviations and to eliminate the appearance of the so called ghost tags. The proposed enhanced algorithm will be implemented on a FPGA (field programmable gate array), more specific the Altera Cyclone EP1CT144C6 FPGA. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2009.

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: In South Africa, black-back jackal (BBJ) predation of sheep causes heavy losses to sheep farmers. Different control measures such as shooting, gin-traps and poisoning have been used to control the jackal population; however, these techniques also kill many harmless animals, as they fail to differentiate between BBJ and harmless animals. In this project, a system is implemented to detect black-backed jackal faces in images. The system was implemented using the Viola-Jones object detection algorithm. This algorithm was originally developed to detect human faces, but can also be used to detect a variety of other objects. The three important key features of the Viola-Jones algorithm are the representation of an image as a so-called ”integral image”, the use of the Adaboost boosting algorithm for feature selection, and the use of a cascade of classifiers to reduce false alarms. In this project, Python code has been developed to extract the Haar-features from BBJ images by acting as a classifier to distinguish between a BBJ and the background. Furthermore, the feature selection is done using the Asymboost instead of the Adaboost algorithm so as to achieve a high detection rate and low false positive rate. A cascade of strong classifiers is trained using a cascade learning algorithm. The inclusion of a special fifth feature Haar feature, adapted to the relative spacing of the jackal’s eyes, improves accuracy further. The final system detects 78% of the jackal faces, while only 0.006% of other image frames are wrongly identified as faces. / AFRIKAANSE OPSOMMING: Swartrugjakkalse veroorsaak swaar vee-verliese in Suid Afrika. Teenmaatreels soos jag, slagysters en vergiftiging word algemeen gebruik, maar is nie selektief genoeg nie en dood dus ook vele nie-teiken spesies. In hierdie projek is ’n stelsel ontwikkel om swartrugjakkals gesigte te vind op statiese beelde. Die Viola-Jones deteksie algoritme, aanvanklik ontwikkel vir die deteksie van mens-gesigte, is hiervoor gebruik. Drie sleutel-aspekte van hierdie algoritme is die voorstelling van ’n beeld deur middel van ’n sogenaamde integraalbeeld, die gebruik van die ”Adaboost” algoritme om gepaste kenmerke te selekteer, en die gebruik van ’n kaskade van klassifiseerders om vals-alarm tempos te verlaag. In hierdie projek is Python kode ontwikkel om die nuttigste ”Haar”-kenmerke vir die deteksie van dié jakkalse te onttrek. Eksperimente is gedoen om die nuttigheid van die ”Asymboost” algoritme met die van die ”Adaboost” algoritme te kontrasteer. ’n Kaskade van klassifiseerders is vir beide van hierdie tegnieke afgerig en vergelyk. Die resultate toon dat die kenmerke wat die ”Asymboost” algoritme oplewer, tot laer vals-alarm tempos lei. Die byvoeging van ’n spesiale vyfde tipe Haar-kenmerk, wat aangepas is by die relatiewe spasieëring van die jakkals se oë, verhoog die akkuraatheid verder. Die uiteindelike stelsel vind 78% van die gesigte terwyl slegs 0.006% ander beeld-raampies verkeerdelik as gesigte geklassifiseer word.