  A change detection system takes as input two images of a region captured at
two different times, and predicts which pixels in the region have undergone
change over the time period. Since pixel-based analysis can be erroneous due to
noise, illumination difference and other factors, contextual information is
usually used to determine the class of a pixel (changed or not). This
contextual information is taken into account by considering a pixel of the
difference image along with its neighborhood. With the help of ground truth
information, the labeled patterns are generated. Finally, Broad Learning
classifier is used to get prediction about the class of each pixel. Results
show that Broad Learning can classify the data set with a significantly higher
F-Score than that of Multilayer Perceptron. Performance comparison has also
been made with other popular classifiers, namely Multilayer Perceptron and
Random Forest.

Computer Vision and Pattern Recognition