Generating High Quality Visible Images from SAR Images Using CNNs
نویسندگان
چکیده
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. Keywords—Synthetic aperture radar image, despeckling, colorization.
منابع مشابه
Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which...
متن کاملHeterogeneous Face Recognition with CNNs
Heterogeneous face recognition aims to recognize faces across different sensor modalities. Typically, gallery images are normal visible spectrum images, and probe images are infrared images or sketches. Recently significant improvements in visible spectrum face recognition have been obtained by CNNs learned from very large training datasets. In this paper, we are interested in the question to w...
متن کاملCystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملFusion of Thermal Infrared and Visible Images Based on Multi-scale Transform and Sparse Representation
Due to the differences between the visible and thermal infrared images, combination of these two types of images is essential for better understanding the characteristics of targets and the environment. Thermal infrared images have most importance to distinguish targets from the background based on the radiation differences, which work well in all-weather and day/night conditions also in land s...
متن کاملChange Detection in Urban Area Using Decision Level Fusion of Change Maps Extracted from Optic and SAR Images
The last few decades witnessed high urban growth rates in many countries. Urban growth can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The purpose of this research is to detect the urban change that is used for urban planning. Change detection using remote sensing images can be classified into three methods: algebra-based, transfor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1802.10036 شماره
صفحات -
تاریخ انتشار 2018