27,861 research outputs found
Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
Infrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
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
An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection
Detecting small moving targets accurately in infrared (IR) image sequences is
a significant challenge. To address this problem, we propose a novel method
called spatial-temporal local feature difference (STLFD) with adaptive
background suppression (ABS). Our approach utilizes filters in the spatial and
temporal domains and performs pixel-level ABS on the output to enhance the
contrast between the target and the background. The proposed method comprises
three steps. First, we obtain three temporal frame images based on the current
frame image and extract two feature maps using the designed spatial domain and
temporal domain filters. Next, we fuse the information of the spatial domain
and temporal domain to produce the spatial-temporal feature maps and suppress
noise using our pixel-level ABS module. Finally, we obtain the segmented binary
map by applying a threshold. Our experimental results demonstrate that the
proposed method outperforms existing state-of-the-art methods for infrared
small-moving target detection
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects
An Examination of Environmental Applications for Uncooled Thermal Infrared Remote Sensing Instruments
Advancements in system design for thermal instruments require assessment of potential environmental applications and appropriate data processing techniques. A novel multi-band thermal imaging system was proposed by DRS Leonardo for the National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program, for which these criteria were assessed. The Multi-Band Uncooled Radiometer Imager (MURI) is a six spectral channel instrument designed to collect images in the thermal infrared, specifically in the range of 7.5 to 12.5 μm. The work detailed in this thesis characterizes the ability of a thermal imager with an uncooled microbolometer focal plane array to provide valuable data for environmental science applications. Here, a pair of studies using simulated data demonstrates the ability of a multispectral instrument such as MURI to detect enhanced levels of atmospheric methane using a novel approach that performs similarly to a state of the art algorithm when applied to MURI data. The novel method is evaluated using a controlled concentration simulated dataset to determine the extent of its detection capabilities and its dependence on atmospheric conditions. The methane investigations reveal the system is capable of detecting a 20 m thick CH4 plume of 10-20 ppm above background levels when column water vapor is low using both the NDMI and matched filter approaches. Additionally, land surface temperature and emissivity retrieval techniques were applied to experimental MURI data recorded during initial test flights to assess their accuracy with MURI data. Utilizing split window and Temperature Emissivity Separation make this examination distinct as this establishes that proven methods can be applied to uncooled multiband imager data. Application of these methods to MURI data demonstrated the system is capable of temperature retrievals with Root Mean Square Errors of less than 1 K to measured reference values and surface emissivity retrievals within 2% of reference database values. The definition and application of the Normalized Differential Methane Index in this thesis demonstrates a novel approach for detection of enhanced plumes of methane utilizing a multispectral system with only a single band allocated to methane absorption features
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
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