Change detection using deep learning approach with object-based image analysis

Document Type

Article

Publication Date

4-2021

Department

College of Forest Resources and Environmental Science

Abstract

In their applications, both deep learning techniques and object-based image analysis (OBIA) have shown better performance separately than conventional methods on change detection tasks. However, efforts to investigate the effect of combining these two techniques for advancing change detection techniques are unexplored in current literature. This study proposes a novel change detection method implementing change feature extraction using convolutional neural networks under an OBIA framework. To demonstrate the effectiveness of our proposed method, we compare the proposed method against benchmark pixel-based counterparts on aerial images for the task of multi-class change detection. To thoroughly assess the performance of our proposed method, this study also for the first time compared three common feature fusion schemes for change detection architecture: concatenation, differencing, and Long Short-Term Memory (LSTM). The proposed method was also tested on simulated misregistered images to evaluate its robustness, a factor that plays an important role in compromising change detection accuracy but has not been investigated for supervised change detection methods in the literature. Finally, the proposed change detection method was also tested using very high resolution (VHR) satellite images for binary class change detection to map an impacted area caused by natural disaster and the result was evaluated using reference data from the Federal Emergency Management Agency (FEMA). With the experimental results from these two sets of experiments, we showed that (1) our proposed method achieved substantially higher accuracy and computational efficiency when compared to pixel-based methods, (2) three feature fusion schemes did not show a significant difference for overall accuracy, (3) our proposed method was robust in image misregistration in both testing and training data, (4) we demonstrate the potential impact of automation to decision making by deploying our method to map a large geographic area affected by a recent natural disaster.

Publisher's Statement

© 2021 Elsevier Inc. Publisher’s version of record: https://doi.org/10.1016/j.rse.2021.112308

Publication Title

Remote Sensing of Environment

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