# Broad Neural Network for Change Detection in Aerial Images

## TL;DR

This paper introduces a broad neural network approach for change detection in aerial images, leveraging contextual information and a broad learning classifier to improve pixel classification accuracy over traditional methods.

## Contribution

The paper proposes a novel broad learning classifier for change detection that outperforms multilayer perceptrons and random forests in accuracy.

## Key findings

- Broad Learning achieves higher F-Score than Multilayer Perceptron.
- Performance surpasses Random Forest classifier.
- Effective use of contextual neighborhood information.

## Abstract

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.