A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches

Synthetic aperture radar (SAR) images are widely used in remote sensing. Interpreting SAR images can be challenging due to their intrinsic speckle noise and grayscale nature. To address this issue, SAR colorization has emerged as a research direction to colorize gray scale SAR images while preserving the original spatial information and radiometric information. However, this research field is still in its early stages, and many limitations can be highlighted. In this paper, we propose a full research line for supervised learning-based approaches to SAR colorization. Our approach includes a protocol for generating synthetic color SAR images, several baselines, and an effective method based on the conditional generative adversarial network (cGAN) for SAR colorization. We also propose numerical assessment metrics for the problem at hand. To our knowledge, this is the first attempt to propose a research line for SAR colorization that includes a protocol, a benchmark, and a complete performance evaluation. Our extensive tests demonstrate the effectiveness of our proposed cGAN-based network for SAR colorization. The code will be made publicly available.Comment: 16 pages, 16 figures, 6 table

An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images

Due to the reduction of technological costs and the increase of satellites launches, satellite images are becoming more popular and easier to obtain. Besides serving benevolent purposes, satellite data can also be used for malicious reasons such as misinformation. As a matter of fact, satellite images can be easily manipulated relying on general image editing tools. Moreover, with the surge of Deep Neural Networks (DNNs) that can generate realistic synthetic imagery belonging to various domains, additional threats related to the diffusion of synthetically generated satellite images are emerging. In this paper, we review the State of the Art (SOTA) on the generation and manipulation of satellite images. In particular, we focus on both the generation of synthetic satellite imagery from scratch, and the semantic manipulation of satellite images by means of image-transfer technologies, including the transformation of images obtained from one type of sensor to another one. We also describe forensic detection techniques that have been researched so far to classify and detect synthetic image forgeries. While we focus mostly on forensic techniques explicitly tailored to the detection of AI-generated synthetic contents, we also review some methods designed for general splicing detection, which can in principle also be used to spot AI manipulate imagesComment: 25 pages, 17 figures, 5 tables, APSIPA 202

Automatic Image Colorization

Množstvo rôznych metód bolo navrhnutých na ofarbovanie obrázkov. V mojej bakalárskej práci implementujem automatické kolorizovanie obrázkov pomocou generatívnych kontradiktórnych sietí - GANov. Ukázali sľubné výsledky pri generovaní rôznych dát, vrátane obrázkov. Používam dve modfikácie GANov - DCGAN a CycleGAN. Tieto dve metódy porovnávam a vyhodnocujem pomocou najpoužívanješích metrík, vhodných pre tento problém. V záverečne jčasti práce sú zobrazené aj ukážkové obrázky, vygenerované jednotlivými modelmi.Many different methods have been suggested to colorize images yet. In this thesis, I try to implement a fully automatic image colorization using generative adversarial networks - GANs. They have shown promising results in generating various kinds of data, including images. I adopt two different modifications of GANs - DCGAN and CycleGAN. These two methods are compared, and results are evaluated using the most common metrics used for this problem. Example images are provided as well

Photo Enhancement On Mobile Devices Using Deep Neural Networks

In recent years, the return of the usage of Artificial Neural Networks has lead to the greatest improvements in the field of Artificial Intelligence, due to the huge diversity of different applications that deep learning models has in a large variety of research fields, and also the evolution of information processing systems capacity. This thesis aims to study which deep neural networks models are most suitable for photo enhancement, to generate images with certain desired characteristics. Model selection has been done by comparing the both supervised, Convolutional Neural Networks, and unsupervised models, Generative Adversarial Networks. It has been demonstrated that Generative Adversarial Networks have great potential by showing results that compete with the state of the art. The chosen model is a Generative Adversarial model which outperforms the rest in terms of a combination of enhancement quality and time taken in the process. Moreover, since the model is compatible with mobile devices it has been integrated and evaluated in a BQ smartphone, to proof its viability on mobile devices.Doble Grado en Ingeniería Informática y Administración de Empresa

SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks - Optimization, Opportunities and Limits

Due to its all time capability, synthetic aperture radar (SAR) remote sensing plays an important role in Earth observation. The ability to interpret the data is limited, even for experts, as the human eye is not familiar to the impact of distance-dependent imaging, signal intensities detected in the radar spectrum as well as image characteristics related to speckle or steps of post-processing. This paper is concerned with machine learning for SAR-to-optical image-to-image translation in order to support the interpretation and analysis of original data. A conditional adversarial network is adopted and optimized in order to generate alternative SAR image representations based on the combination of SAR images (starting point) and optical images (reference) for training. Following this strategy, the focus is set on the value of empirical knowledge for initialization, the impact of results on follow-up applications, and the discussion of opportunities/drawbacks related to this application of deep learning. Case study results are shown for high resolution (SAR: TerraSAR-X, optical: ALOS PRISM) and low resolution (Sentinel-1 and -2) data. The properties of the alternative image representation are evaluated based on feedback from experts in SAR remote sensing and the impact on road extraction as an example for follow-up applications. The results provide the basis to explain fundamental limitations affecting the SAR-to-optical image translation idea but also indicate benefits from alternative SAR image representations