Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images
| Autor: | Kyungsu Lee, Jihwan P. Choi, Jun Hee Kim, Haeyun Lee, Juhum Park, Younghwan Na, Jae Youn Hwang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
Computer science Remote sensing application Feature extraction Geophysics. Cosmic physics 0211 other engineering and technologies 02 engineering and technology Convolutional neural network remote sensing Similarity (network science) 0202 electrical engineering electronic engineering information engineering Computers in Earth Sciences TC1501-1800 021101 geological & geomatics engineering Remote sensing business.industry QC801-809 Deep learning Cosine similarity Siamese network Ocean engineering Feature (computer vision) Change detection 020201 artificial intelligence & image processing Artificial intelligence business similarity attention |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4139-4149 (2021) |
| ISSN: | 2151-1535 |
| Popis: | Change detection is an important task in the field of remote sensing. Various change detection methods based on convolutional neural networks (CNNs) have recently been proposed for remote sensing using satellite or aerial images. However, existing methods allow only the partial use of content information in images during change detection because they adopt simple feature similarity measurements or pixel-level loss functions to construct their network architectures. Therefore, when these methods are applied to complex urban areas, their performance in terms of change detection tends to be limited. In this article, a novel CNN-based change detection approach, referred to as a local similarity Siamese network (LSS-Net), with a cosine similarity measurement, was proposed for better urban land change detection in remote sensing images. To use content information on two sequential images, a new change attention map-based content loss function was developed in this study. In addition, to enhance the performance of the LSS-Net in terms of change detection, a suitable feature similarity measurement method, incorporated into a local similarity attention module, was determined through systemic experiments. To verify the change detection performance of the LSS-Net, it was compared with other state-of-the-art methods. The experimental results show that the proposed method outperforms the state-of-the-art methods in terms of the F1 score (0.9630, 0.9377, and 0.7751) and kappa (0.9581, 0.9351, and 0.7646) on the three test datasets, thus suggesting its potential for various remote sensing applications. |
| Databáze: | OpenAIRE |
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