6,026 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Feature Extraction for image super-resolution using finite rate of innovation principles
To understand a real-world scene from several multiview pictures, it is necessary to find
the disparities existing between each pair of images so that they are correctly related to one
another. This process, called image registration, requires the extraction of some specific
information about the scene. This is achieved by taking features out of the acquired
images. Thus, the quality of the registration depends largely on the accuracy of the
extracted features.
Feature extraction can be formulated as a sampling problem for which perfect re-
construction of the desired features is wanted. The recent sampling theory for signals with
finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame-
work for the extraction of features in real images. This thesis first focuses on extending the
sampling theory for FRI signals to a multichannel case and then presents exact sampling
results for two different types of image features used for registration: moments and edges.
In the first part, it is shown that the geometric moments of an observed scene can
be retrieved exactly from sampled images and used as global features for registration. The
second part describes how edges can also be retrieved perfectly from sampled images for
registration purposes. The proposed feature extraction schemes therefore allow in theory
the exact registration of images. Indeed, various simulations show that the proposed
extraction/registration methods overcome traditional ones, especially at low-resolution.
These characteristics make such feature extraction techniques very appropriate for
applications like image super-resolution for which a very precise registration is needed. The
quality of the super-resolved images obtained using the proposed feature extraction meth-
ods is improved by comparison with other approaches. Finally, the notion of polyphase
components is used to adapt the image acquisition model to the characteristics of real
digital cameras in order to run super-resolution experiments on real images
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling
L'abstract è presente nell'allegato / the abstract is in the attachmen
UAV based distributed automatic target detection algorithm under realistic simulated environmental effects
Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air [1]. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection [2], image/signal processing and data fusion techniques [3]. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The automatic target detector is a cascade of classifiers based on Haar-like features. The detector\u27s performance is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic camera and environmental conditions. In order to improve automatic target detection (ATD) performance in a swarmed UAV system, we propose and design several fusion techniques both at the image and score level and analyze both the case of a single observation and the case of multiple observations of the same target
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