Reduced complexity turbo equalization using a dynamic Bayesian network

It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.Additional file 1: DBN-TE Pseudocode algorithm. (a) DBN-TE function pseudocode. (b) FORWARD MESSAGE function pseudocode. (c) BACKWARD MESSAGE function pseudocode. (d) FORWARD BACKWARD MESSAGE function pseudocode. (e) LLR ESTIMATES function pseudocode.http://www.hindawi.com/journals/asp/am2013ai201

Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method

Recent development has identified the benefits of using hyper-temporal satellite time series data for land cover change detection and classification in South Africa. In particular, the monitoring of human settlement expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of land-cover change in this province which covers an area of roughly 125 000km2. In this paper, a spatiotemporal autocorrelation change detection (STACD) method is developed to improve the performance of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement which is then validated by an operator using additional data (such as high resolution imagery). Importantly, as the objective of the method is to indicate areas of potential change to operators for further analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD formulation.http://www.elsevier.com/locate/jag2016-08-30hb201

Land cover separability analysis of MODIS time series data using a combined simple harmonic oscillator and a mean reverting stochastic process

It is proposed that the time series extracted from moderate resolution imaging spectroradiometer satellite data be modeled as a simple harmonic oscillator with additive colored noise. The colored noise is modeled with an Ornstein–Uhlenbeck process. The Fourier transform and maximum-likelihood parameter estimation are used to estimate the harmonic and noise parameters of the colored simple harmonic oscillator. Two case studies in South Africa show that reliable class differentiation can be obtained between natural vegetation and settlement land cover types, when using the parameters of the colored simple harmonic oscillator as input features to a classifier. The two case studies were conducted in the Gauteng and Limpopo provinces of South Africa. In the case of the Gauteng case study, we obtained an average for single-band classification, while standard harmonic features only achieved an average . In conclusion, the results obtained from the colored simple harmonic oscillator approach outperformed standard harmonic features and the minimum distance classifier.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443ai201

An inductive approach to simulating multispectral MODIS surface reflectance time series

In this paper, a first order MODIS time series simulator, which uses a Colored Simple Harmonic Oscillator, is proposed. The simulated data can be used to augment data sets so that data intensive classification and change detection algorithms can be applied without enlarging the available ground truth data sets. The simulator’s validity is tested by simulating data sets of natural vegetation and human settlement areas and comparing it to the ground truth data in the Gauteng province located in South Africa. The difference found between the real and simulated data sets, which is reported in the experiments is negligent. The simulated and real world data sets are compared by using a wide selection of class and pixel metrics. In particular the average temporal Hellinger distance between the real and simulated data sets is 0.2364 and 0.2269 for the vegetation and settlement class respectively, while the average parameter Hellinger distance is 0.1835 and 0.2554 respectively.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

Cavalieri integration

We use Cavalieri’s principle to develop a novel integration technique which we call Cavalieri integration. Cavalieri integrals differ from Riemann integrals in that non-rectangular integration strips are used. In this way we can use single Cavalieri integrals to find the areas of some interesting regions for which it is difficult to construct single Riemann integrals. We also present two methods of evaluating a Cavalieri integral by first transforming it to either an equivalent Riemann or Riemann-Stieltjes integral by using special transformation functions h(x) and its inverse g(x), respectively. Interestingly enough it is often very difficult to find the transformation function h(x), whereas it is very simple to obtain its inverse g(x).http://www.tandfonline.com/loi/tqma20hb201

Using Page's cumulative sum test on MODIS time series to detect land-cover changes

Human settlement expansion is one of the most pervasive forms of land cover change in South Africa. The use of Page’s Cumulative Sum Test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500 m MODIS time series satellite data. The method is a sequential per pixel change alarm algorithm that can take into account positive detection delay, probability of detection and false alarm probability to construct a threshold. Simulated change data was generated to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before mentioned study areas.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.The CSIR Strategic Research Panelhttp://www.elsevier.com/locate/jagai201

Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data

A method for detecting land cover change using NDVI time series data derived from 500m MODIS satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time series of a 3x3 grid of MODIS pixels. The NDVI time series for each of these pixels was modeled as a triply (mean, phase and amplitude) modulated cosine function, and an extended Kalman Filter was used to estimate the parameters of the modulated cosine function through time. A spatial comparison between the center pixel of the the 3x3 grid and each of its neighboring pixel’s mean and amplitude parameter sequence was done to calculate a change metric which yields a change or no-change decision after thresholding. Although the development of new settlements is the most prevalent form of land cover change in South Africa, it is rarely mapped and known examples amounts to a limited number of changed MODIS pixels. Therefore simulated change data was generated and used for preliminary optimization of the change detection method. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 89% change detection accuracy, while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=885

Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion

The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hb201

Land cover change detection using autocorrelation analysis on MODIS time-series data : detection of new human settlements in the Gauteng province of South Africa

Human settlement expansion is one of the most pervasive forms of land cover change in the Gauteng province of South Africa. A method for detecting new settlement developments in areas that are typically covered by natural vegetation using 500 m MODIS time-series satellite data is proposed. The method is a per pixel change alarm that uses the temporal autocorrelation to infer a change index which yields a change or no-change decision after thresholding. Simulated change data was generated and used to determine a threshold during an off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 92% change detection accuracy with a 15% false alarm rate. The method shows good performance when compared to a traditional NDVI differencing method that achieved a 75% change detection accuracy with a 24% false alarm rate for the same study area.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443hb2017Electrical, Electronic and Computer Engineerin