  In this letter, a novel method for change detection is proposed using
neighborhood structure correlation. Because structure features are insensitive
to the intensity differences between bi-temporal images, we perform the
correlation analysis on structure features rather than intensity information.
First, we extract the structure feature maps by using multi-orientated gradient
information. Then, the structure feature maps are used to obtain the
Neighborhood Structural Correlation Image (NSCI), which can represent the
context structure information. In addition, we introduce a measure named
matching error which can be used to improve neighborhood information.
Subsequently, a change detection model based on the random forest is
constructed. The NSCI feature and matching error are used as the model inputs
for training and prediction. Finally, the decision tree voting is used to
produce the change detection result. To evaluate the performance of the
proposed method, it was compared with three state-of-the-art change detection
methods. The experimental results on two datasets demonstrated the
effectiveness and robustness of the proposed method.
