Hyper-spectral image processing applications on the SIMD Pixel Processor for the digital battlefield

2003 ◽  
Author(s):  
S.M. Chai ◽  
A. Gentile ◽  
W.E. Lugo-Beauchamp ◽  
J.L. Cruz-Rivera ◽  
D.S. Wills
Keyword(s):  
Image Processing ◽  
Spectral Image ◽  
Hyper Spectral

scholarly journals Simulation of the Photodetection in the PPS CMOS Sensors

2013 ◽  
Vol 8-9 ◽  
pp. 611-618
Author(s):  
Florin Toadere ◽  
Radu Arsinte
Keyword(s):  
Image Processing ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Spectral Image ◽  
Image Capture ◽  
Visible Image ◽  
Optical Part ◽  
Electrical Part

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

Band selection by mutual information for hyper-spectral image classification

2016 ◽  
Vol 8 (1) ◽  
pp. 98 ◽  
Author(s):  
Ibtissam Banit' ◽  
N.A. ouagua ◽  
Mounir Ait Kerroum ◽  
Ahmed Hammouch ◽  
Driss Aboutajdine
Keyword(s):  
Mutual Information ◽  
Image Classification ◽  
Band Selection ◽  
Spectral Image ◽  
Hyper Spectral

Hyper-spectral image classification using spatial-spectral manifold reconstruction

2018 ◽  
Vol 26 (7) ◽  
pp. 1827-1836
Author(s):  
黄 鸿 HUANG Hong ◽  
陈美利 CHEN Mei-li ◽  
段宇乐 DUAN Yu-le ◽  
石光耀 SHI Guang-yao
Keyword(s):  
Image Classification ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Manifold Reconstruction

scholarly journals The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

2018 ◽  
Vol 173 ◽  
pp. 03071
Author(s):  
Wu Wenbin ◽  
Yue Wu ◽  
Jintao Li
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Compression Algorithm ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Spectral Image ◽  
Compression Algorithms ◽  
Serial Processing ◽  
Compression Time ◽  
Hyper Spectral

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

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