Jan C. Olivier

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 500-m MODIS satellite data is proposed. The algorithm acts as a per-pixel change alarm and takes the NDVI time series of a 3 × 3 grid of MODIS pixels as the input. 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 3 × 3 grid and each of its neighboring pixels 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 amount to a limited number of changed MODIS pixels. Therefore, simulated change data were generated and used for the 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 an 89% change detection accuracy while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.

Unsupervised Land Cover Change Detection: Meaningful Sequential Time Series Analysis

An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short term Fourier transform coefficients computed over subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy exceeding 76% on real land cover conversion and more than 70% on simulated land cover conversion.

Detection of phase singularities with a Shack-Hartmann wavefront sensor

While adaptive optical systems are able to remove moderate wavefront distortions in scintillated optical beams, phase singularities that appear in strongly scintillated beams can severely degrade the performance of such an adaptive optical system. Therefore the detection of these phase singularities is an important aspect of strong-scintillation adaptive optics. We investigate the detection of phase singularities with the aid of a Shack-Hartmann wavefront sensor and show that, in spite of some systematic deficiencies inherent to the Shack-Hartmann wavefront sensor, it can be used for the reliable detection of phase singularities, irrespective of their morphologies. We provide full analytical results, together with numerical simulations of the detection process.

Improving Land Cover Class Separation Using an Extended Kalman Filter on MODIS NDVI Time-Series Data

It is proposed that the normalized difference vegetation index time series derived from Moderate Resolution Imaging Spectroradiometer satellite data can be modeled as a triply (mean, phase, and amplitude) modulated cosine function. Second, a nonlinear extended Kalman filter is developed to estimate the parameters of the modulated cosine function as a function of time. It is shown that the maximum separability of the parameters for natural vegetation and settlement land cover types is better than that of methods based on the fast Fourier transform using data from two study areas in South Africa.

Single antenna interference cancellation for synchronised GSM networks using a widely linear receiver

A novel cochannel single antenna interference cancellation (SAIC) receiver is proposed for synchronised Group Special Mobile (GSM) systems. The receiver uses a two-stage strategy, where in the first stage cochannel interference is cancelled by a widely linear filter, while inter-symbol interference due to the GSM frequency-selective Rayleigh-fading environment is removed by a second-stage equaliser. Analytical results for the optimal widely linear filter coefficients are derived. Simulation results show excellent performance with large gains over the conventional receiver under interference limited channel conditions. It is shown that the conventional maximum likelihood sequence estimator or maximum a posteriori probability receiver is optimal when cochannel interference it not the dominant impairment, and it is proposed that the SAIC algorithm is disabled when the estimated carrier-to-interference (C/I) ratio is above a certain threshold.

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 change 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.

Efficient equalization and symbol detection for 8-PSK EDGE cellular system

A new method is presented for channel equalization and symbol detection of an enhanced data rate for Global System for Mobile (GSM) communication and IS-136 evolution (EDGE) cellular system in which eight phase-shift keying (8-PSK) modulation is employed. The new method iteratively minimizes the Euclidean distance between the detected and received signal sequences, with neighbor symbol perturbation to reduce the computational complexity. The new algorithm is computationally efficient and can also be easily implemented into commercial signal processors. Simulation results comparing our method with the reduced-state sequence-estimation (RSSE) method, both with and without set partitioning, are presented.

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 Pages cumulative sum (CUSUM) test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500-m Moderate Resolution Imaging Spectroradiometer 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 were generated to determine a threshold during a preliminary offline 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.

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.

Time-varying and frequency-selective channel estimation with unequally spaced pilot symbols

In this paper, an accurate and computationally efficient algorithm is proposed for estimating time-varying and frequency-selective fading channel with unequally spaced pilot symbols. By employing the time-varying coefficient polynomial interpolation method, it is proved that the time-varying channel impulse response can be estimated by the product of a constant matrix and the fading information at pilot symbol positions. Furthermore, a least square off-line training algorithm is presented to optimally calculate the constant matrix, taking into consideration of the statistics of channel fading and noise. The new algorithm can also be applied for estimating flat fading channel with equally spaced pilot symbols as a special case. Simulation results indicate that our new channel estimation algorithm leads to small mean square error for fading estimation and provides bit error rate performance close to that of the perfect channel estimation.