100 research outputs found
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
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
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
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
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
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
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