SOMDNCD: Image Change Detection Based on Self-Organizing Maps and Deep Neural Networks
| Autor: | Lin Xinhong, Ruliang Xiao, Cui Runxi, Youcong Ni, Mingwei Lin, Lifei Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Synthetic aperture radar
Self-organizing map General Computer Science Computer science 0211 other engineering and technologies 02 engineering and technology unsupervised learning Image change detection Constant false alarm rate self-organizing map 0202 electrical engineering electronic engineering information engineering Median filter General Materials Science 021101 geological & geomatics engineering Artificial neural network Pixel business.industry General Engineering deep neural network Speckle noise Pattern recognition median filter 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Focus (optics) lcsh:TK1-9971 Change detection |
| Zdroj: | IEEE Access, Vol 6, Pp 35915-35925 (2018) |
| ISSN: | 2169-3536 |
| Popis: | Image change detection is a research hotspot in many fields of application, such as environmental monitoring, disaster investigation, urban research, and more. How to reduce the influence of speckle noise when conducting change detection in an acquired synthetic aperture radar (SAR) image is a challenging issue. This research shows that reasonably balancing noise suppression with the preservation of the edges of regions is the key to generating a good change map. Therefore, a new image detection method based on a self-organizing map and deep neural network (SOMDNCD) is proposed. First, the method uses a median filter to improve the difference image that is generated by the mean-ratio operator, which reduces the influence of the image point noise on generating difference maps. Compared with the difference map formed by the logarithmic ratio operator, the edge information in the image is excellently retained and the missed detection rate is reduced; second, the network preprocesses the difference map, obtains a preliminary change map, and divides the pixels of the difference map into three types: no change, noise, and change. Finally, a deep neural network is used to train a noise-like training set on the network to reduce the residual noise in the change class and obtain the final change graph. The experimental results show that compared with other current mainstream methods, the proposed SOMDNCD change detection method directly addresses noise and is universal for a variety of data sets. The proposed method exhibits a lower missed detection rate in the SAR image data set and a more ideal false alarm rate than other methods. |
| Databáze: | OpenAIRE |
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