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MICHAEL T. EISMANN
Author Info
Open Visualization
Name
Affiliation
Papers
MICHAEL T. EISMANN
USAF, Res Lab, 2241 Av Circle, Wright Patterson AFB, OH 45433 USA
17
Collaborators
Citations
PageRank
22
326
19.71
Referers
Referees
References
608
308
164
Search Limit
100
608
Publications (17 rows)
Collaborators (22 rows)
Referers (100 rows)
Referees (100 rows)
Title
Citations
PageRank
Year
Spectral Radiance Modeling and Bayesian Model Averaging for Longwave Infrared Hyperspectral Imagery and Subpixel Target Identification.
1
0.35
2017
A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images.
33
1.22
2016
Extension of the Linear Chromodynamics Model for Spectral Change Detection in the Presence of Residual Spatial Misregistration
2
0.39
2015
Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection
1
0.36
2015
Evaluating the Lidar/HSI direct method for physics-based scene modeling
0
0.34
2014
Application of Model-Based Change Detection to Airborne VNIR/SWIR Hyperspectral Imagery
6
0.45
2012
Removing parallax-induced changes in Hyperspectral Change Detection
1
0.38
2012
Hyperspectral imaging phenomenology for the detection and tracking of pedestrians
0
0.34
2012
Fusion of LIDAR data with hyperspectral and high-resolution imagery for automation of DIRSIG scene generation
0
0.34
2012
Detecting Changes in Hyperspectral Imagery Using a Model-Based Approach
26
1.19
2011
Automated hyperspectral target detection and change detection from an airborne platform: Progress and challenges
1
0.48
2010
Automated Hyperspectral Cueing for Civilian Search and Rescue
53
2.07
2009
Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations
35
2.23
2008
Hyperspectral Change Detection: Methodology and Challenges
3
0.50
2008
Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions
54
2.54
2005
MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor
69
4.03
2004
Application of the stochastic mixing model to hyperspectral resolution enhancement
41
2.51
2004
1