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Siamese Transformer Network for Hyperspectral Image Target Detection
IEEE Transactions on Geoscience and Remote Sensing, 2022Hyperspectral target detection can be described as locating targets of interest within a hyperspectral image based on prior information of targets. The complexity of actual scenes limits the performance of traditional statistical methods that rely on ...
Weiqiang Rao +5 more
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Optical Engineering, 2012
Hundreds of simple target-detection algorithms were tested on mid- and long-wave forward-looking infrared images. Each algorithm is briefly described. Indications are given as to which performed well. Most of these simple algorithms are loosely derived from standard tests of the difference of two populations. For target detection, these are populations
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Hundreds of simple target-detection algorithms were tested on mid- and long-wave forward-looking infrared images. Each algorithm is briefly described. Indications are given as to which performed well. Most of these simple algorithms are loosely derived from standard tests of the difference of two populations. For target detection, these are populations
openaire +1 more source
Self-Trained Target Detection of Radar and Sonar Images Using Automatic Deep Learning
IEEE Transactions on Geoscience and Remote Sensing, 2022Recent deep learning (DL) detectors adopted by radar or sonar (RS) are normally trained with transfer learning, where the typical workflow is to pretrain a convolutional neural network (CNN) on external large-scale classification datasets (e.g., ImageNet)
Peng Zhang +5 more
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Toward Dense Moving Infrared Small Target Detection: New Datasets and Baseline
IEEE Transactions on Geoscience and Remote SensingAs an important research branch of infrared small target detection, dense target detection (e.g., drone swarm detection) has always been a topic worth exploring.
Shengjia Chen +5 more
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Auditory target detection in reverberation
The Journal of the Acoustical Society of America, 2004Measurements and theoretical predictions of auditory target detection in simulated reverberant conditions are reported. The target signals were pulsed 13-octave bands of noise and the masker signal was a continuous wideband noise. Target and masker signals were passed through a software simulation of a reverberant room with a rigid sphere modeling a ...
Zurek, PM, Freyman, RL, Balakrishnan, U
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Detection of a Distributed Target
IEEE Transactions on Aerospace and Electronic Systems, 1971The influence of increasing range resolution on the detectability of targets with dimensions greater than the resolution cell is studied. An N-cell target model is assumed, which contains k reflecting cells, each reflecting independently according to the same Rayleigh amplitude distribution. It will be referred to as the (N,k) target.
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Target detection in hyperspectral images
2016 24th Signal Processing and Communication Application Conference (SIU), 2016Target detection in hyperspectral images is important in many applications including search and rescue operations, defense systems, mineral exploration, mine detection and border security. In this study, the goal is to detect the nine sub-pixel targets, from seven different materials, that are placed around the town.
YÜKSEL ERDEM, SENİHA ESEN +1 more
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A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window
IEEE Geoscience and Remote Sensing Letters, 2020Local contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before local contrast calculation and may easily be disturbed by noises.
Jin-Hui Han +5 more
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DETECTING RESIDUES IN TARGETING PEPTIDES
Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, 2005Knowledge of targeting signals is of immense importance for understanding the cellular processes by which proteins are sorted and transported. This paper presents a system of recurrent neural networks which demonstrate an ability to detect residues belonging to specific targeting peptides with greater accuracy than current feed forward models.
Boden, M. B., Hawkins, J. C.
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