Target detection using integrated hyper spectral sensing and processing

2005 ◽  
Author(s):  
Abhijit Mahalanobis ◽  
Robert Muise
Keyword(s):  
Target Detection ◽  
Hyper Spectral ◽  
Spectral Sensing

scholarly journals А Nоvel Data-Driven Орtimаl Methоdоlоgy fоr Deteсting Shiр from Sаr Images Bаsed on Аrtifiсiаl Intelligenсe

2021 ◽  
Vol 1 (3) ◽  
pp. 17-22
Author(s):  
M.S.Antony Vigil ◽  
Rishabh Jain ◽  
Tanmay Agarwal ◽  
Abhinav Chandra
Keyword(s):  
Target Detection ◽  
Detection Rate ◽  
Target Identification ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Data Driven ◽  
Identification Rate ◽  
Ship Detection ◽  
Hyper Spectral ◽  
Multiple Issues

There are a variety of deep learning algorithms available in the supervision of ships, but they are dealing with multiple issues of inaccurate identification rate and inadequate target detection speed. At this stage, an algorithm is given оn Соnvоlutiоnаl Neural Network for target identification and detection using the ship image. The study involves the investigation of the reactions of hyper spectral atmospheric rectification on the accurate and precise results of ship detection. The ship features which were detected from two atmospheric rectified algorithms on airborne hyper spectral data were corrected by the application of these algorithms with the help of an unsupervised target detection procedure. High accuracy and fast ship identification was a result of this algorithm and using unique modules, improving the loss function and enlargement of data for the smaller targets. The results of the experiments show that our algorithm has given much better detection rate as compared to target detection algorithm using traditional machine learning.

Salad leaf disease detection using machine learning based hyper spectral sensing

2014 ◽  
Author(s):  
Ritaban Dutta ◽  
Daniel Smith ◽  
Yanfeng Shu ◽  
Qing Liu ◽  
Petra Doust ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Detection ◽  
Leaf Disease ◽  
Hyper Spectral ◽  
Spectral Sensing

A Euclidean Distance based Super Resolution Method for Sub pixel target detection in Hyper Spectral Images

GIS Business ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 104-124
Author(s):  
Dr. K. C. Tiwari ◽  
Amrita Bhandari
Keyword(s):  
Spatial Distribution ◽  
Target Detection ◽  
Euclidean Distance ◽  
Super Resolution ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Resolution Method ◽  
Mixed Pixel ◽  
Detection Algorithms ◽  
Hyper Spectral

Most target detection algorithms suffer from the limitation that they can detect only the full pixels of the target while the target may also reside, besides the full pixel, partially in several surrounding pixels. In some cases, the target may even be embedded completely within the pixel. Both these cases are known as subpixel target detection problem. Many target detection applications, however, require detection of full pixels as well as detection of subpixel targets in the surrounding pixels which constitute a case of the mixed pixel. The problem is addressed by full pixel detection followed by spectral unmixing to determine the abundance fraction of the target. Though spectral unmixing gives the abundance fractions, it still does not give the spatial distribution/ arrangement of subpixels of the target with the surrounding pixels. The process of optimizing the spatial distribution of subpixels inside any given pixel based on the available abundance fractions is known as super resolution. This paper investigates Inverse Euclidean distance based super resolution. The algorithm performs well at different scale factors both for synthetic and real hyperspectral data which can aid the super resolution process significantly and thereby enhance the identification and recognition of target. A comparative assessment is also performed with Pixel Swap algorithm.

Selective Attention and Error Processing in an Illusory Conjunction Task

2005 ◽  
Vol 19 (3) ◽  
pp. 216-231 ◽  
Author(s):  
Albertus A. Wijers ◽  
Maarten A.S. Boksem
Keyword(s):  
Target Detection ◽  
Event Related Potentials ◽  
Illusory Conjunction ◽  
Conjunction Error ◽  
Error Related Negativity ◽  
Display Element ◽  
Related Potentials ◽  
Feature Error ◽  
High Level ◽  
Correct Target

Abstract. We recorded event-related potentials in an illusory conjunction task, in which subjects were cued on each trial to search for a particular colored letter in a subsequently presented test array, consisting of three different letters in three different colors. In a proportion of trials the target letter was present and in other trials none of the relevant features were present. In still other trials one of the features (color or letter identity) were present or both features were present but not combined in the same display element. When relevant features were present this resulted in an early posterior selection negativity (SN) and a frontal selection positivity (FSP). When a target was presented, this resulted in a FSP that was enhanced after 250 ms as compared to when both relevant features were present but not combined in the same display element. This suggests that this effect reflects an extra process of attending to both features bound to the same object. There were no differences between the ERPs in feature error and conjunction error trials, contrary to the idea that these two types of errors are due to different (perceptual and attentional) mechanisms. The P300 in conjunction error trials was much reduced relative to the P300 in correct target detection trials. A similar, error-related negativity-like component was visible in the response-locked averages in correct target detection trials, in feature error trials, and in conjunction error trials. Dipole modeling of this component resulted in a source in a deep medial-frontal location. These results suggested that this type of task induces a high level of response conflict, in which decision-related processes may play a major role.

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