2,678 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
Investigating SAR algorithm for spaceborne interferometric oil spill detection
The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 dat
A novel marine radar targets extraction approach based on sequential images and Bayesian Network
This research proposes a Bayesian Network-based methodology to extract moving vessels from a plethora of blips captured in frame-by-frame radar images. First, the inter-frame differences or graph characteristics of blips, such as velocity, direction, and shape, are quantified and selected as nodes to construct a Directed Acyclic Graph (DAG), which is used for reasoning the probability of a blip being a moving vessel. Particularly, an unequal-distance discretisation method is proposed to reduce the intervals of a blip’s characteristics for avoiding the combinatorial explosion problem. Then, the undetermined DAG structure and parameters are learned from manually verified data samples. Finally, based on the probabilities reasoned by the DAG, judgments on blips being moving vessels are determined by an appropriate threshold on a Receiver Operating Characteristic (ROC) curve. The unique strength of the proposed methodology includes laying the foundation of targets extraction on original radar images and verified records without making any unrealistic assumptions on objects' states. A real case study has been conducted to validate the effectiveness and accuracy of the proposed methodology
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Classification of polarimetric synthetic aperture radar (PolSAR) images is an
active research area with a major role in environmental applications. The
traditional Machine Learning (ML) methods proposed in this domain generally
focus on utilizing highly discriminative features to improve the classification
performance, but this task is complicated by the well-known "curse of
dimensionality" phenomena. Other approaches based on deep Convolutional Neural
Networks (CNNs) have certain limitations and drawbacks, such as high
computational complexity, an unfeasibly large training set with ground-truth
labels, and special hardware requirements. In this work, to address the
limitations of traditional ML and deep CNN based methods, a novel and
systematic classification framework is proposed for the classification of
PolSAR images, based on a compact and adaptive implementation of CNNs using a
sliding-window classification approach. The proposed approach has three
advantages. First, there is no requirement for an extensive feature extraction
process. Second, it is computationally efficient due to utilized compact
configurations. In particular, the proposed compact and adaptive CNN model is
designed to achieve the maximum classification accuracy with minimum training
and computational complexity. This is of considerable importance considering
the high costs involved in labelling in PolSAR classification. Finally, the
proposed approach can perform classification using smaller window sizes than
deep CNNs. Experimental evaluations have been performed over the most
commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band
data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained
overall accuracies range between 92.33 - 99.39% for these benchmark study
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