Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network

2021 ◽  
Author(s):  
Srinitya G ◽  
Sharmila D ◽  
Logeswari S ◽  
Daniel Madan Raja S
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Convolution Neural Network ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Segmentation Algorithms ◽  
Aperture Radar

scholarly journals A New Classification Approach with Deep Mask R-CNN for Synthetic Aperture Radar Image Segmentation

2020 ◽  
Vol 26 (6) ◽  
pp. 52-57
Author(s):  
Ridvan Yayla ◽  
Baha Sen
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
High Accuracy ◽  
Synthetic Aperture ◽  
Classification Approach ◽  
Radar Images ◽  
Learning Techniques ◽  
Segmentation Methods ◽  
Aperture Radar

In this paper, a hybrid classification approach which is combined with a more deep mask region-convolutional neural network and sparsity driven despeckling algorithm is proposed for synthetic aperture radar (SAR) image segmentation instead of the classical segmentation methods. In satellite technology, synthetic aperture radar images are strongly used for a lot of areas, such as evaluating air conditions, determining agricultural fields, climatic changes, and as a target in the military. Synthetic aperture radar images must be segmented to each meaningful point in the image for a quality segmentation process. In contrast, synthetic aperture radar images have a lot of noisy speckles and these speckles should be also reduced for a quality segmentation. Current studies show that deep learning techniques are widely used for segmentation methods. High accuracy and fast results can be obtained with deep learning techniques for image segmentation. Mask region-convolutional neural network can not only separate each meaningful field in the image, but it can also generate a high accuracy prediction for each meaningful field of synthetic aperture radar images. The study shows that smoothed SAR images can be classified as multiple regions with deep neural networks.

A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

1999 ◽  
Vol 12 (3) ◽  
pp. 499-511 ◽  
Author(s):  
Ennio Mingolla ◽  
William Ross ◽  
Stephen Grossberg
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Aperture Radar
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