211 research outputs found
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
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
This work has been accepted by IEEE TGRS for publication. The majority of
optical observations acquired via spaceborne earth imagery are affected by
clouds. While there is numerous prior work on reconstructing cloud-covered
information, previous studies are oftentimes confined to narrowly-defined
regions of interest, raising the question of whether an approach can generalize
to a diverse set of observations acquired at variable cloud coverage or in
different regions and seasons. We target the challenge of generalization by
curating a large novel data set for training new cloud removal approaches and
evaluate on two recently proposed performance metrics of image quality and
diversity. Our data set is the first publically available to contain a global
sample of co-registered radar and optical observations, cloudy as well as
cloud-free. Based on the observation that cloud coverage varies widely between
clear skies and absolute coverage, we propose a novel model that can deal with
either extremes and evaluate its performance on our proposed data set. Finally,
we demonstrate the superiority of training models on real over synthetic data,
underlining the need for a carefully curated data set of real observations. To
facilitate future research, our data set is made available onlineComment: This work has been accepted by IEEE TGRS for publicatio
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation
In this paper, we present the optical image simulation from a synthetic
aperture radar (SAR) data using deep learning based methods. Two models, i.e.,
optical image simulation directly from the SAR data and from multi-temporal
SARoptical data, are proposed to testify the possibilities. The deep learning
based methods that we chose to achieve the models are a convolutional neural
network (CNN) with a residual architecture and a conditional generative
adversarial network (cGAN). We validate our models using the Sentinel-1 and -2
datasets. The experiments demonstrate that the model with multi-temporal
SAR-optical data can successfully simulate the optical image, meanwhile, the
model with simple SAR data as input failed. The optical image simulation
results indicate the possibility of SARoptical information blending for the
subsequent applications such as large-scale cloud removal, and optical data
temporal superresolution. We also investigate the sensitivity of the proposed
models against the training samples, and reveal possible future directions
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Recent advances in Generative Adversarial Learning allow for new modalities
of image super-resolution by learning low to high resolution mappings. In this
paper we present our work using Generative Adversarial Networks (GANs) with
applications to overhead and satellite imagery. We have experimented with
several state-of-the-art architectures. We propose a GAN-based architecture
using densely connected convolutional neural networks (DenseNets) to be able to
super-resolve overhead imagery with a factor of up to 8x. We have also
investigated resolution limits of these networks. We report results on several
publicly available datasets, including SpaceNet data and IARPA Multi-View
Stereo Challenge, and compare performance with other state-of-the-art
architectures.Comment: 9 pages, 9 figures, WACV 2018 submissio
- …