289 research outputs found
Multiscale segmentation of SAR imagery
Caption title.Includes bibliographical references (p. [11]).Sponsored in part by Advanced Research Projects Agency under Air Force Contract. F19628-95-C-0002 Sponsored in part by Air Force Office of Scientific Research Grant. F49620-93-1-0604, F49620-95-1-0083C.H. Fosgate ... [et al.]
Scale-based robust image segmentation
Caption title.Includes bibliographical references (leaf [6]).Supported by DARPA. F49620-93-1-0064A. Kim ... [et al.]
Knowledge-based segmentation of SAR data with learned priors
©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.821747An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data
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
Deep learning-based change detection in remote sensing images:a review
Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods
Remote Sensing of the Oceans
This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements
A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data
Includes bibliographical references (p. 29-30).Supported by Air Force Office of Scientific Research. F49620-95-1-0083, F49620-96-1-0028 (awarded to Boston University) Supported by a National Defense Science and Engineering Graduate Fellowship awarded by the Defense Advanced Research Projects Agency. F49620-93-1-0604Austin B. Frakt, W. Clem Karl, Alan S. Willsky
Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector
Oil spill detection has attracted increasing attention in recent years since
marine oil spill accidents severely affect environments, natural resources, and
the lives of coastal inhabitants. Hyperspectral remote sensing images provide
rich spectral information which is beneficial for the monitoring of oil spills
in complex ocean scenarios. However, most of the existing approaches are based
on supervised and semi-supervised frameworks to detect oil spills from
hyperspectral images (HSIs), which require a huge amount of effort to annotate
a certain number of high-quality training sets. In this study, we make the
first attempt to develop an unsupervised oil spill detection method based on
isolation forest for HSIs. First, considering that the noise level varies among
different bands, a noise variance estimation method is exploited to evaluate
the noise level of different bands, and the bands corrupted by severe noise are
removed. Second, kernel principal component analysis (KPCA) is employed to
reduce the high dimensionality of the HSIs. Then, the probability of each pixel
belonging to one of the classes of seawater and oil spills is estimated with
the isolation forest, and a set of pseudo-labeled training samples is
automatically produced using the clustering algorithm on the detected
probability. Finally, an initial detection map can be obtained by performing
the support vector machine (SVM) on the dimension-reduced data, and then, the
initial detection result is further optimized with the extended random walker
(ERW) model so as to improve the detection accuracy of oil spills. Experiments
on airborne hyperspectral oil spill data (HOSD) created by ourselves
demonstrate that the proposed method obtains superior detection performance
with respect to other state-of-the-art detection approaches
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