DeepAI AI Chat
Log In Sign Up

Generating High Quality Visible Images from SAR Images Using CNNs

02/27/2018
by   Puyang Wang, et al.
Rutgers University
0

We propose a novel approach for generating high quality visible-like images from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on a cascaded network of convolutional neural nets (CNNs) for despeckling and image colorization. The cascaded structure results in faster convergence during training and produces high quality visible images from the corresponding SAR images. Experimental results on both simulated and real SAR images show that the proposed method can produce visible-like images better compared to the recent state-of-the-art deep learning-based methods.

READ FULL TEXT

page 1

page 2

page 4

page 5

07/20/2018

Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in...
01/23/2022

Transformer-based SAR Image Despeckling

Synthetic Aperture Radar (SAR) images are usually degraded by a multipli...
04/17/2020

Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm

SAR images are affected by multiplicative noise that impairs their inter...
11/10/2020

Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images ...
12/02/2017

Fruit recognition from images using deep learning

In this paper we introduce a new, high-quality, dataset of images contai...
02/03/2020

A deep learning method for image-based subject-specific local SAR assessment

PURPOSE: Local specific absorption rate (SAR) cannot be measured and is ...
11/22/2020

Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

Small area change detection from synthetic aperture radar (SAR) is a hig...