TY - GEN
T1 - Semantic segmentation and segmentation refinement using machine learning case study
T2 - 2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2019
AU - Bona, Daniel Sande
AU - Mursanto, Petrus
AU - Murni, Aniati
PY - 2019/10
Y1 - 2019/10
N2 - Classical methods for image segmentation such as pixel thresholding, clustering, region growing, maximum likelihood have been used regularly and relied on for a long time. However, these classical methods have limitations, particularly on images where there are many overlapping pixel values between features, which is common in remote sensing images. The advent of machine learning, in particular, deep learning in computer vision and image analysis, has gained interest in the remote sensing field. Current deep learning architecture has been able to achieve high accuracy for image recognition, object detection, and segmentation. This study performed image segmentation on the coastal area with high water turbidity using Landsat-8 images. Currently, the standard tool to derive water turbidity data from Landsat-8 images is the level-2 plugin of SEADAS software. However, due to its rigorous processing method, the processing time using SEADAS Level-2 Plugin is quite long; for example, processing one Landsat-8 image took around 8 hours. As a consequence, the amount of time needed to process multiple images is increasing. Deep learning has advantages once the model trained, the inference or prediction process is quite fast. Therefore it has the potential to be used as a complementary tool to predict and segment high turbidity areas, because in deep learning. In this study, we implemented U-Net architecture with ResNet connection and used Generative-Adversarial Network (GAN) to refined segmentation results.
AB - Classical methods for image segmentation such as pixel thresholding, clustering, region growing, maximum likelihood have been used regularly and relied on for a long time. However, these classical methods have limitations, particularly on images where there are many overlapping pixel values between features, which is common in remote sensing images. The advent of machine learning, in particular, deep learning in computer vision and image analysis, has gained interest in the remote sensing field. Current deep learning architecture has been able to achieve high accuracy for image recognition, object detection, and segmentation. This study performed image segmentation on the coastal area with high water turbidity using Landsat-8 images. Currently, the standard tool to derive water turbidity data from Landsat-8 images is the level-2 plugin of SEADAS software. However, due to its rigorous processing method, the processing time using SEADAS Level-2 Plugin is quite long; for example, processing one Landsat-8 image took around 8 hours. As a consequence, the amount of time needed to process multiple images is increasing. Deep learning has advantages once the model trained, the inference or prediction process is quite fast. Therefore it has the potential to be used as a complementary tool to predict and segment high turbidity areas, because in deep learning. In this study, we implemented U-Net architecture with ResNet connection and used Generative-Adversarial Network (GAN) to refined segmentation results.
KW - CNN
KW - Deep learning
KW - GAN
KW - Image segmentation
KW - Machine learning
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85077499784&partnerID=8YFLogxK
U2 - 10.1109/ICARES.2019.8914551
DO - 10.1109/ICARES.2019.8914551
M3 - Conference contribution
T3 - Proceedings of the 2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2019
BT - Proceedings of the 2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2019 through 18 October 2019
ER -