Exploiting Neighborhood Structural Features for Change Detection

TL;DR

This paper introduces a change detection method leveraging neighborhood structural features and correlation analysis, which is robust to intensity variations and improves detection accuracy using random forest classification.

Contribution

The paper presents a novel change detection approach based on neighborhood structure correlation and introduces the NSCI feature and matching error for improved robustness.

Findings

Outperforms three state-of-the-art methods in experiments

Effective in diverse datasets

Robust to intensity differences

Abstract

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.