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We apply some state-of-the-art semantic segmentation methods to InSAR image building segmentation, directly. They are Unet, SegNet, RefineNet, PSPNet, and Deeplab v3+. These segmentation patterns are regarded as the comparative results with CVCMFFNet, which links to https://github.com/Jiankun-chen/CVCMFFNet-master
We present a novel complex-valued convolutional and multi-feature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with other state-of-the-art networks. The comparative experiments are link to https://github.com/Jiankun-chen/building-semantic-segmentation-of-InSAR-images
A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlow framework, a bilayer supervised learning SNN is constructed from scratch. We take the lead in the application of SAR image classification and conduct experiments on the MSTAR dataset.
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