Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images
| Autor: | L.V. Yunrong, Yuanfang Chen, Jian-Feng Wang, Muhammad Alam, Cong Guangpei |
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| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Computer Networks and Communications Computer science business.industry Deep learning Pooling ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Image processing 02 engineering and technology Convolutional neural network Hardware and Architecture Remote sensing (archaeology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Encoder Software 021101 geological & geomatics engineering Information Systems Remote sensing |
| Zdroj: | Mobile Networks and Applications. 26:200-215 |
| ISSN: | 1572-8153 1383-469X |
| Popis: | In recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models. |
| Databáze: | OpenAIRE |
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