A fast target detection and imaging method for compressive sensing Earth observation

2014 ◽  
Author(s):  
Chuanrong Li ◽  
Qi Wang ◽  
Changyong Cao ◽  
Xi Shao ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Earth Observation ◽  
Imaging Method

SISSI Project: A Feasibility Study for a Super Resolved Compressive Sensing Multispectral Imager in the Medium Infrared

2021 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Cinzia Lastri ◽  
Gabriele Amato ◽  
Massimo Baldi ◽  
Tiziano Bianchi ◽  
Maria Fabrizia Buongiorno ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressive Sensing ◽  
Feasibility Study ◽  
Spatial Resolution ◽  
Spatial Light Modulator ◽  
Super Resolution ◽  
Earth Observation ◽  
Light Modulator ◽  
The Core ◽  
Multispectral Imager

This paper describes the activities related to a feasibility study for an Earth observation optical payload, operating in the medium infrared, based on super-resolution and compressive sensing techniques. The presented activities are running in the framework of the ASI project SISSI, aiming to improve ground spatial resolution and mitigate saturation/blooming effects. The core of the payload is a spatial light modulator (SLM): a bidimensional array of micromirrors electronically actuated. Thanks to compressive sensing approach, the proposed payload eliminates the compression board, saving mass, memory and energy consumption.

scholarly journals Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2168 ◽  
Author(s):  
Chuanyun Wang ◽  
Tian Wang ◽  
Ershen Wang ◽  
Enyan Sun ◽  
Zhen Luo
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Gaussian Mixture ◽  
Visual Surveillance ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.

scholarly journals Compressive Sensing Based Three-Dimensional Imaging Method with Electro-Optic Modulation for Nonscanning Laser Radar

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 748
Author(s):  
Yulong An ◽  
Yanmei Zhang ◽  
Haichao Guo ◽  
Jing Wang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Low Cost ◽  
Functional Model ◽  
Three Dimensional ◽  
Visible Spectrum ◽  
Depth Map ◽  
Imaging Method ◽  
Practical Applications ◽  
Electro Optic

Low-cost Laser Detection and Ranging (LiDAR) is crucial to three-dimensional (3D) imaging in applications such as remote sensing, target detection, and machine vision. In conventional nonscanning time-of-flight (TOF) LiDAR, the intensity map is obtained by a detector array and the depth map is measured in the time domain which requires costly sensors and short laser pulses. To overcome such limitations, this paper presents a nonscanning 3D laser imaging method that combines compressive sensing (CS) techniques and electro-optic modulation. In this novel scheme, electro-optic modulation is applied to map the range information into the intensity of echo pulses symmetrically and the measurements of pattern projection with symmetrical structure are received by the low bandwidth detector. The 3D imaging can be extracted from two gain modulated images that are recovered by solving underdetermined inverse problems. An integrated regularization model is proposed for the recovery problems and the minimization functional model is solved by a proposed algorithm applying the alternating direction method of multiplier (ADMM) technique. The simulation results on various subrates for 3D imaging indicate that our proposed method is feasible and achieves performance improvement over conventional methods in systems with hardware limitations. This novel method will be highly valuable for practical applications with advantages of low cost and flexible structure at wavelengths beyond visible spectrum.

scholarly journals Exploring the Potential of Compressive Sensing and Super-Resolution for Space Applications in the MIR-TIR

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 35
Author(s):  
Lastri ◽  
Guzzi ◽  
Nardino ◽  
Palombi ◽  
Raimondi
Keyword(s):  
Compressive Sensing ◽  
Focal Plane ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Earth Observation ◽  
Space Applications ◽  
The Matrix ◽  
Pros And Cons ◽  
Matrix Detector ◽  
Resolution Data

Earth Observation applications could take advantage from the availability of high spatial resolution data in the MIR-TIR. This, however, poses technological challenges, especially for the availability of large focal plane. Here we discuss two approaches, the compressive sensing and the super-resolution, that could provide a basis for the development of innovative EO instruments working in the MIR-TIR and able to enjoy an augmented resolution without increasing the number of detection elements in the matrix detector. Main pros and cons are analyzed, focusing on simulations done for an instrument combining both approaches to achieve super-resolved images for studying hot events.

Transmitted Sequence Influence to Sonar Target Detection using Compressive Sensing

2019 ◽  
Author(s):  
Isidora Stankovic ◽  
Jitendra Singh Sewada ◽  
Matt Geen ◽  
Cornel Ioana ◽  
Milos Dakovic ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Transmitted Sequence
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