Sidike Paheding (Patrick), Ph.D.
​---
​Assistant Research Professor
Remote Sensing Lab
Dept. of Earth & Atmospheric Sciences
Saint Louis University
St. Louis, MO 63108
​
                                                                                                                                   News
     Paper entitled, ''UAV-Based high resolution thermal imaging for vegetation monitoring and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640 and thermoMap Cameras " has been accepted by Remote Sensing. - Feb 2019.

    Paper entitled, ''The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches" has been accepted by Electronics. - Jan 2019.

     Paper entitled, "dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 satellite imagery" has been  accepted by Remote Sensing of Environment.  [A top journal in Imaging Science and Remote Sensing]. [IF: 6.4]  - Nov. 2018

     Paper entitled, "Machine learning based ensemble prediction of water quality variables with proximal remote sensing using feature-level and decision-level fusion" has been accepted by Photogrammetric Engineering & Remote Sensing. [IF: 3.2]  - Sept. 2018

    Paper entitled, "Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine" has been accepted by Remote Sensing. [IF: 3.4]  - Sept. 2018

   Paper entitled, "Progressively Expanded Neural Network for hyperspectral image classification: a new neural network paradigm for remote sensing image analysis." has been accepted by ISPRS Journal of Photogrammetry and Remote Sensing.  [IF: 5.9]  - Sept. 2018
​
    Paper entitled, "Handwritten Bangla digit recognition using the state-of-the-art deep convolutional neural networks" has been accepted by Computational Intelligence and Neuroscience. [IF: 1.649] - Aug. 2018

   Paper entitled "Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Aerial Imagery" has been accepted by Photogrammetric Engineering and Remote Sensing.  [IF: 3.2] - July. 2018
​
   Paper entitled "Adaptive Trigonometric Transformation Function based Image Enhancement for Unmanned Aerial System Imagery" has been accepted by IEEE Geoscience and Remote Sensing Letter. [IF: 2.89] - March 2018

 Certificate in Deep Learning Specialization 

Research Interests 

 Computer Vision, Machine Learning, Deep Learning, Remote Sensing,  ​Feature Extraction, Biomedical Imaging.

Research Highlights

dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 satellite imagery

A novel deep learning method, namely deep progressively expanded network (dPEN), is proposed for mapping nineteen different objects including crop types, weeds and crop residues, in a heterogeneous agricultural field using WorldView-3 (WV-3) imagery.The results demonstrated that: (1) The proposed dPEN allows for building a deeper neural network from multispectral data which was the limitation of many convolutional neural networks; (2) dPEN was able to extract more discriminative features from VNIR and SWIR bands by producing the highest overall accuracy (OA: 86.06%) over competing methods such as support vector machine and random forest; (3) The inclusion of WV-3 SWIR bands greatly improved the classification accuracy; (4) SWIR bands were particularly beneficial to improve the classification accuracy of some individual classes such as weeds, crop residues, and corn and soybean during late developmental stages; (5) The red-edge band (705–745 nm) was identified as the most important band affecting the classification accuracy nearly 10%, whereas the coastal band (400–450 nm) provided the lowest contribution; and (6) SWIR-5 band (2145–2185 nm) contributed most to OA by enhancing it approximately 4% when combined with VNIR bands, while SWIR-1 (1195–1225 nm) yielded the lowest improvement (1.55%) for OA. These research outcomes provide useful information for efficiently mapping agricultural landscape, and indicate the potential practices of dPEN and contributions of spectral bands in WV-3 for plant phenotyping, weed control, and crop residue retention.​ [Paper]
Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine
Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.. [Paper]
Progressively Expanded Neural Network  (PEN Net) for hyperspectral image classification:
​a new neural network paradigm for remote sensing image analysis
A novel neural network based classification algorithm, named Progressively Expanded Neural Network (PEN Net), is proposed. PEN Net can effectively interpret hyperspectral pixels in nonlinear feature spaces and then determine their categories. Furthermore, a spectral-spatial hyperspectral image (HSI) classification framework is introduced to test the generality and robustness of the PEN Net. Experimental results on four standard hyperspectral data sets illustrate that: (1) PEN Net classifier yields better accuracy and competitive processing speed in HSI classification tasks compared to the state-of-the-art methods; (2) Multi-hidden layer based PEN Net generally provides better performance than single hidden layer one; (3) Combination of spectral and spatial features in the PEN Net classifier can significantly improve the classification accuracy by 6–15% compared to the spectral only based HSI classification. This study implies that the proposed neural network architecture opens a new window for future research and the potential for remote sensing image analysis. [Paper]
Adaptive Trigonometric Transformation Function based Image Enhancement for Unmanned Aerial System Imagery 
Unmanned Aerial System (UAS) based imaging technology has gained great interests in modern photogrammetry and remote sensing. However, due to the limitations of UAS imaging devices, Image Enhancement (IE) has become a necessary process for improving the visual appearance of UAS images. We propose a new adaptive yet highly efficient luminance enhancement method, namely Adaptive Trigonometric Transformation Function (ATTF), for enhancing UAS based images . [Paper] 

​Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

This study evaluates the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. ​Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, Extreme learning regression yields promising results compared to alternate regression models. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment. ​ [Paper]

​  Volumetric Directional Pattern for Spatial Feature Extraction from Hyperspectral Imagery

In this work, we propose a novel feature extraction method, named Volumetric Directional Pattern, for Hyperspectral Image (HSI) classification. The proposed technique fuses the texture information from three consecutive bands in the input HSI. The extracted local image texture features for each pixel of interest is then fed into an extreme learning machine classifier to assign object category. Experimental results demonstrate that the effectiveness of VDP compared to the state-of-the-arts. [Paper]
Single Image Super Resolution
This work presents a machine learning-based approach to reconstruct a high-resolution (HR) image from a single low- resolution (LR) image. Inspired by the human visual cortex system, which is sensitive to high-frequency (HF) components in an image, we aim to model this concept by training a neural network to estimate the missing HF components that contain structural details. In our method, various directional edge responses at each pixel are considered to obtain more complete HF information and then a regularized extreme learning regression model is trained using a set of LR and HR images. [Paper]
Multiclass Object Detection with Single Query from Hyperspectral Imagery
We propose a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object's spectral signature. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results demonstrate the superiority of the proposed CSFJTC technique compared to the state-of-the-arts. [Paper]

Education

  • Ph.D., Electrical Engineering , University of Dayton,  U.S.A, Aug. 2013 - Dec. 2016
    • ​Ph.D. Research  covers Machine Learning, Computer Vision and Remote Sensing

Professional Services​

Journal Editorship:
 Associate Editor, Signal, Image and Video Processing (Springer) (IF = 1.643) 
​
Guest Editor:
Invited Journal Reviewer [Verified Review Record from Publon]
  • ISPRS Journal of Photogrammetry and Remote Sensing
  • IEEE Geoscience and Remote Sensing Letters
  • Remote Sensing
  • Sensors
  • Applied Sciences
  • Algorithms
  • IEEE Transactions on Medical Imaging 
  • Photogrammetric Engineering & Remote Sensing
  • Signal, Image and Video Processing
  • Information Fusion 
  • Journal of Imaging
  • Journal of Real-Time Image Processing
  • Journal of Optics and Laser Technology
  • Journal of Electrical and Computer Engineering
  • Journal of Fire Sciences
  • ISPRS International Journal of Geo-Information
  • Multimodal Technologies and Interaction

Invited Conference Reviewer
  • International Conference on Pattern Recognition (ICPR)
  • IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Perception Beyond Visible Spectrum (PBVC)
  • International Symposium on Visual Computing (ISVC)
  • International Conference on Computer and Information Technology (ICCIT)
  • International Conference on Advances in Electrical Engineering (ICAEE)

​Conference Technical Program Committee Member
  • International Conference on Computer and Information Technology, 2014 - Present

Professional Membership

  • Member of Tau Beta Pi (Engineering Honor Society), November 2012 (initiate), life member
  • Member of SPIE (Society of Photographic Instrumentation Engineers), 2011 - 2016
  • Member of IEEE (Institute of Electrical and Electronics Engineers), 2011 - 2016

Teaching Experience​

  • Instructor, Depart of Earth and Atmospheric Sciences,  Saint Louis University, Spring 2019.
    • Geospatial Method: Digital Image Processing
  • Co-Instructor, Depart of Earth and Atmospheric Sciences,  Saint Louis University, Summer 2018.
    • Machine Learning for GIS and Remote Sensing 
  • Graduate Teaching Assistant, Department of Electrical and Computer Engineering, University of Dayton.
    • Advanced Digital Image Processing (Selected Topics in Computer Vision)
    • Machine Learning and Pattern Recognition
  • Graduate Teaching Assistant, Department of Electrical and Computer Engineering,​ University of South Alabama
    • Digital Image Processing
    • Computer Architecture
    • Digital Logic Design
    • Circuit and System Design

Publications


Papers Under Peer-Review
4. Maimaitijiang, M., Sagan, V., Paheding Sidike, et al. (2018). Unmanned Aerial System (UAS)-based crop yield prediction using multi-sensor data fusion and deep neural network . Feb 2019.
3. Maimaitiyiming, M., Sagan, V., Paheding Sidike, Kwasniewski, M.  "Dual activation functions-based Extreme Learning Machine (ELM) for estimating grapevine berry yield and quality under different irrigation treatments and rootstocks conditions", 2019. 
2. Maimaitijiang, M., Sagan, V., Paheding Sidike, et al. "Photogrammetry point cloud based crop volume model and biomass estimation using high resolution imagery from low-cost unmanned aerial system", 2019.  (major revision)
1. Sean Hartling, Sagan, V., Paheding Sidike, et al. "Machine learning based forest tree species mapping using Worldview-3 and airborne LiDAR data fusion." 2019.  (major revision).

Book Chapter
78. Paheding Sidike, Mohammad Alom and Vasit Sagan, "Robust pattern recognition via joint transform correlation," Sept. 2018. Nova Science Publishers.

Peer-reviewed journal Publications
77.  Sagan, V., Maimaitijiang, M., Paheding Sidike, Eblimit, K., Peterson, K.T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., Pauli, D., Ward, R., Fritschi, F., Shakoor, N., Mockler, T. (2019). UAV-Based high resolution thermal imaging for vegetation monitoring and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640 and thermoMap Cameras. Remote Sensing, Feb 2019. (in press).
76. M.Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, Paheding Sidike, M. S.. Nasrin, B. C Van Esesn, A.A S. Awwal, V. K. Asari. "The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches." Electronics, Jan 2019. (in press).
75. Paheding Sidike, Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., Mockler, T., Fritschi, F. (2018). dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 imagery. Remote Sensing of Environment, vol. 221, 756-772, 2019. [A novel deep learning paradigm and in-depth analysis of WV-3 satellite  imagery for crop monitoring] 
74. Peterson, K.T., Sagan, V., Paheding Sidike, et al., "Machine learning based ensemble prediction of water quality variables with proximal remote sensing using feature-level and decision-level fusion. " Photogrammetric Engineering & Remote Sensing, Sept .2018. (In press)
73. Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., Martinez, M., "Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine, " Remote Sensing,  vol.10(10), 1503, 2018.
72. Paheding Sidike, V. K. Asari, Vasit  Sagan, "Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification:  a new neural network paradigm for remote sensing image analysis." ISPRS Journal of Photogrammetry and Remote Sensing, vol. 146, 161-181, Sept. 2018. [A new neural network paradigm for Image Analysis] 
71. Zahangir Alom, Paheding Sidike, Mahmudul Hasan, Tarek Taha and Vijayan K. Asari, “Handwritten Bangla digit recognition using the state-of-the-art d eep convolutional neural networks,” Computational Intelligence and Neuroscience, vol. 2018, 1-13, Aug. 2018. 
70. Fatema Albalooshi, Paheding Sidike, Vasit Sagan, Yousif Albastaki and Vijayan Asari. “Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Aerial Imagery," Photogrammetric Engineering & Remote Sensing,  vol. 84, No. 7,  pp. 451-458(8),  July 2018.   [New formulation of Active Contour Model in Deep Learning Framework] 
69. Paheding Sidike, V. Sagan, M. Qumsiyeh, M. Maimaitijiang, A. Essa, and V. Asari, “Adaptive Trigonometric Transformation Function with Image Contrast and Color Enhancement: Application to Unmanned Aerial System Imagery,” IEEE Geoscience and Remote Sensing Letters,  vol. 15, Issue: 3, p.404-408, Mar.  2018.  [Fast and effective illumination enhancement for UAV  data] 
68. M. Maimaitijiang, A. Ghulam, Paheding Sidike, S. Hartling, M. Maimaitiyiming, K. Peterson, E. Shavers, J. Peterson, J. Burken, F. Fritschi, “Unmanned aerial systems based phenotyping of soybean using multi-sensor data fusion and extreme learning machine,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 134, p.43-58, 2017.   [Impact of RGB, thermal and Multispectral sensors on soybean phenotyping] 
67. Daniel Prince, Paheding Sidike, Almabrok Essa, and Vijayan K. Asari, “Multifeature fusion for automatic building change detection in wide-area imagery,” Journal of Applied Remote Sensing, 11(2), 026040, 2017.
66. ALmabrok Essa, Paheding Sidike, and Vijayan K. Asari, “Volumetric directional pattern for hyperspectral image classification,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 7, p. 1-5, 2017. [New texture feature through spectral direction] 
65. Paheding Sidike, Ghulam, A., Asari, V.K., and Alam, M.S., “Efficient hyperspectral target detection using class-associative spectral fringe-adjusted JTC with dimensionality reduction techniques,” Asian Journal of Physics, vol. 26. no.  3&4,  p.171-180, 2017.
64. Evan Krieger, Paheding Sidike, Theus Aspiras, and Vijayan K. Asari, “Deterministic object tracking using Gaussian ringlet and directional edge features,” Optics & Laser Technology, vol. 95, p. 133-146, May 2017.   [Rotation Invariant object tracking] 
63. Paheding Sidike, Evan Krieger, Zahangir Alom, Vijayan K. Asari and Tarek Taha, “A fast single image super-resolution via directional edge guided regularized extreme learning regression,” Signal, Image and Video Processing , vol. 11, p. 961–968, 2017.   [Neural network based near real-time single image super-resolution] 
62. Zahangir Alom, Paheding Sidike, Tarek Taha and Vijayan K. Asari, “State preserving extreme learning machine: a monotonically increasing learning approach,” Neural Processing Letters, vol. 45, issue 2, p. 703–725, Sept. 2016.  [Very simple but effective ELM method]
61. Paheding Sidike, Vijayan K. Asari, and Mohammad S. Alam, “Recent advances in fringe-adjusted joint transform correlation based optical pattern recognition techniques,” Asian Journal of Physics, vol. 25, no. 4, April 2016. 
60. Paheding Sidike, Vijayan K. Asari, and Mohammad S. Alam, “Multiclass object detection with single query in hyperspectral imagery using class-associative spectral fringe-adjusted joint transform correlation,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol.54, issue 2, p.1196-1208, Feb. 2016. [The first formulation of deterministic multiclass object detection via joint correlation analysis] 
59. ALmabrok Essa, Paheding Sidike, and Vijayan K. Asari, “Efficient key frame selection approach for object detection in wide area surveillance applications,” International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), vol. 3, issue 2, p. 20-34, 2015.
58. Vijayan K. Asari, Paheding Sidike, Chen Cui, and Varun Santhaseelan, “New wide-area surveillance techniques for protection of pipeline infrastructure,” SPIE Newsroom: Defense and Security, p. 1-4, Jan. 2015.
57. Paheding Sidike, M. S. Alam, “Logarithmic fringe-adjusted joint transform correlation,” Optical Engineering, 52(10), p. 103108, 2013.
 
Conference Proceedings
56. M. Burnette, C, Willis, R. Kooper, J.D. Maloney, R. Ward, N. Shakoor, M. Newcomb, G. S. Rohde,  N. Fahlgren, V. Sagan, P. Sidike, J. A. Terstriep, D. LeBauer, "TERRA-REF Data Processing Infrastructure,"  Practice and Experience in Advanced Research Computing (PEARC), July 22-26, 2018. 
55. Paheding Sidike, Vasit Sagan, and Vijayan K. Asari, "A multi-component based volumetric directional pattern for texture feature extraction from hyperspectral imagery," SPIE Defense + Security: Pattern Recognition and Tracking XXIX , Orlando, Florida, United States, 15 - 19 April 2018. 
54. Paheding Sidike, Almabrok Essa, Maher Qumsiyeh and Vijayan Asari, “Extreme learning machine with variance inflation factor for robust pattern recognition, " SPIE Defense + Security: Pattern Recognition and Tracking XXVIII, 9 - 13 April 2017.
53. Fatema Albalooshi, Paheding Sidike, and Vijayan K. Asari, “Automatic Detection and Segmentation of Oil Leak in Ocean Environment,” Middle East Heavy Oil Congress (MEHOC), 11-12 April, 2017.
52. Evan Krieger, Paheding Sidike, Almabrok Essa, and Vijayan K. Asari, “Boosted ringlet features for robust object tracking,” IEEE Applied Imagery Pattern Recognition Workshop: Imaging and Artificial Intelligence: Intersection and Synergy - AIPR 2016, Washington DC, USA, October 18-20, 2016.
51. Paheding Sidike, Chen Chen, Vijayan K. Asari, Yan Xu, and Wei Li, “Classification of hyperspectral image using multiscale spatial texture features,” IEEE 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), August 2016.
50. Paheding Sidike, Daniel Prince, Almabrok Essa, and Vijayan K. Asari, “Automatic building change detection through adaptive local textural features and sequential background removal,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10 - 15 July 2016.
49. Paheding Sidike, Almabrok Essa, Evan W. Krieger, and Vijayan K. Asari, “Tracking visual objects using pyramidal rotation invariant features,” SPIE Defense + Security: Optical Pattern Recognition XXVII, Baltimore, MD, USA, 17 - 21 April 2016.
48. Paheding Sidike, Almabrok Essa, and Vijayan K. Asari, “Object shape extraction using mixture of gradient kernels and adaptive thresholding,” SPIE Defense + Security: Optical Pattern Recognition XXVII, Baltimore, MD, USA, 17 - 21 April 2016.
47. Paheding Sidike, Md. Zahangir Alom, Vijayan K. Asari, and Tarek M. Taha, “Non-regularized state preserving extreme learning machine for natural scene classification,” International Conference on Computer Vision and Image Processing (CVIP), Indian Institute of Technology , Roorkee, Uttarakhand, India, 26 - 28 February 2016. 
46. ALmabrok Essa, Paheding Sidike, and Vijayan K. Asari, “Real time automatic machinery threat detection and identification system for protection of pipeline infrastructure,” IS&T International Conference on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, San Francisco, California, USA, 14-18 February 2016. 
45. Evan Krieger, Paheding Sidike, Theus Aspiras, and Vijayan K. Asari, “Directional ringlet intensity feature transform for tracking,” IEEE International Conference on Image Processing (ICIP), September 2015. 
44. Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari and Tarek Taha, “State preserving extreme learning machine for face recognition,” IEEE International Joint Conference on Neural Networks (IJCNN), July 2015.
43. Almabrok Essa, Paheding Sidike, and Vijayan K. Asari, “A modular approach for key-frame selection in wide area surveillance video analysis,” IEEE National Aerospace & Electronics Conference. June 2015.
42. Paheding Sidike, Almabrok Essa, Fatema Albalooshi, Vijayan K. Asari, and Varun Santhaseelan, “Automatic building change detection in wide area surveillance,” IEEE National Aerospace & Electronics Conference. June 2015.
41. Paheding Sidike, Almabrok Essa, and Vijayan K. Asari, “Intrusion detection in aerial imagery for protecting pipeline infrastructure,” IEEE National Aerospace & Electronics Conference. June 2015. 
40. Evan W. Krieger, Paheding Sidike, Theus Aspiras, and Vijayan K. Asari, “Vehicle tracking under occlusion conditions using directional ringlet intensity feature transform,” IEEE National Aerospace & Electronics Conference. June 2015.
39. Pahading Sidike, Vijayan K. Asari, and Mohammad Alam, “Multiple object detection in hyperspectral imagery using spectral fringe-adjusted joint transform correlator,” IS&T/SPIE International Conference on Electronic Imaging: Image Processing: Machine Vision Applications VIII, 2015.
38. Paheding Sidike, Vijayan K. Asari, and Mohammad S. Alam, “A robust fringe-adjusted joint transform correlator for efficient object detection,” SPIE Conference on Defense + Security: Optical Pattern Recognition XXVI, Baltimore, MD, USA, 20-24 April 2015.
37. Fatema Albalooshi, Evan Krieger, Paheding Sidike and Vijayan K. Asari, “Efficient thermal image segmentation through integration of nonlinear intensity enhancement with unsupervised active contour model,” SPIE Conference on Defense + Security: Optical Pattern Recognition XXVI, Baltimore, MD, USA, 20-24 April 2015.
36. Vijayan K. Asari, Paheding Sidike, Chen Cui, and Varun Santhaseelan, “Recent progress in wide-area surveillance: Protecting our pipeline infrastructure,” IS&T/SPIE International Conference on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, San Francisco, California, USA, 08-12 February 2015.
35. Fatema Albalooshi, Yakov Diskin, Paheding Sidike, and Vijayan K. Asari, “Automatic detection and segmentation of carcinoma in radiographs,” IEEE International Workshop on Applied Imagery and Pattern Recognition (AIPR) Washington DC, 14-16 October, 2014.
34. Paheding Sidike, Yakov Diskin, Saibabu Arigela, and Vijayan K. Asari, “Visibility improvement of shadow regions using hyperspectral band integration,” Proceedings of SPIE Remote Sensing Conference: Image and Signal Processing for Remote Sensing, Vol. 9244-32, 2014.
33. Fatema Albalooshi, Paheding Sidike, and Vijayan K. Asari, “Efficient hyperspectral image segmentation using geometric active contour formulation,” Proceedings of SPIE Remote Sensing Conference: Image and Signal Processing for Remote Sensing, Vol. 9244-6, 2014.
32. Paheding Sidike, Theus Aspiras, Vijayan K. Asari, and M. S. Alam, “A rotation Invariant pattern recognition using fringe-adjusted joint transform correlation and histogram representation,” Proceedings of SPIE, Optical Pattern Recognition XXV, Vol. 9094, 90940F, 2014.
31. Yakov Diskin, Paheding Sidike, Saibabu Arigela, and Vijayan K. Asari, “Shadow removal for illumination invariant face detection in hyperspectral imagery,” Proceedings of OSA Imaging and Applied Optics Congress: Imaging Systems and Applications, IW4C.3, 2014.
30. Paheding Sidike and Vijayan. K. Asari and M. S. Alam, “Illumination invariant pattern recognition using fringe-adjusted joint transform correlation and monogenic signal,” Proceedings of SPIE / IS&T Electronic Imaging, Image Processing: Machine Vision Applications VII, Vol. 9024, 90240C, 2014.
29. Paheding Sidike, M. S. Alam, Chen Cui, and Vijayan. K. Asari, “Efficient face recognition using shifted phase-encoded fringe-adjusted joint transform correlator,” In IEEE Proc. of International Conference on Advances in Electrical Engineering (ICAEE), p.425-430, 2013.
28. Paheding Sidike and M. S. Alam, “Spectral fringe-adjusted joint transform correlation based efficient object classification in hyperspectral imagery,” Proceedings of SPIE, Optical Pattern Recognition XXIV, vol. 8748, 2013. (Travel Award)
27. M. S. Alam and Paheding Sidike, “Trends in oil spill detection via hyperspectral imaging,” IEEE Proc. of 7th International Conference on Electrical and Computer Engineering (ICECE), p.858 - 862, 2012.
26. M. S. Alam, R .P. Gollapalli, and Paheding Sidike, “Identification and detection of oil and oil-derived substances at the surface and subsurface levels via hyperspectral imaging,” Proceedings of SPIE: Optical Pattern Recognition XXIII, vol.8398, p.839802 (1-13), 2012.
25. Paheding Sidike, J. Khan, M. S. Alam, and S. Bhuiyan, “Spectral unmixing of hyperspectral data for oil spill detection,” Proceedings of SPIE: Optics and Photonics for Information Processing VI, vol.8498, p.84981B (1-10), 2012.
24. Paheding Sidike, J. Khan, M. S. Alam, R. Gollapalli, and S. Bhuiyan, “Efficient classification of multispectral imagery for oil spill detection,” In IEEE Proc. of International Conference on Advances in Electrical Engineering (ICAEE), p.141-146, 2011.

Abstracts and Posters Presentation
23.  Kyle T. Peterson, Wasit Wulamu, Paheding Sidike, Elizabeth Hasenmueller, John Sloan, Jason Knouft, "Estimation of Regional Water Quality Variable Concentrations with In-Situ Spectroscopy using a Feature and Decision- Level Fusion Regression Approch," AGU Fall Meeting, New Orleans, USA, Dec. 2017.
22. M. Maimaitijiang, A. Ghulam, Paheding Sidike, S. Hartling, M. Maimaitiyiming, K. Peterson, E. Shavers, J. Peterson, J. Burken, F. Fritschi, “UAS-based Phenotyping of Soybean Using Multi-sensor Data Fusion and Extreme Learning Machine," Missouri EPSCoR annual meeting, Aug. 15, 2017.
21. Paheding Sidike and Vijayan K. Asari, “Multi-spectral data exploitation for automatic object detection and segmentation," Human Perception of Multi-spectral Imagery Workshop Fusion 2016, Dayton, Ohio, December 02, 2016. (Invited Talk)
20. Vijayan K. Asari, Paheding Sidike, Almabrok Essa, and Daniel Prince, “Automatic Pipeline Threat Detection by Aerial Surveillance," (Keynote Talk), 2nd International Conference and Business Expo on Wireless Telecommunication, Dubai, UAE, 22 April 2016. 
19. Almabrok Essa Essa, Paheding Sidike, Daniel P Prince, and Vijayan K. Asari, “Frame Redundancy Elimination Technology for Big Data Analysis,âAI Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
18. Paheding Sidike, Md. Zahangir Alom, Tarak Taha, and Vijayan K. Asari, “An Efficient Brain-like Learning Machine to Mimic Human Perception,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
17. Paheding Sidike, Almabrok Essa Essa, Daniel P Prince, and Vijayan K. Asari, “Object Detection Through 2D-3D Visible and Hyperspectral Imagery,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
16. Evan W Krieger, Paheding Sidike, and Vijayan K. Asari, “Object Tracking using Statistic-based Feature Fusion Technique,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
15. Paheding Sidike, Evan W Krieger, and Vijayan K. Asari, “Image Interpolation Using Fourier Phase Features,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
14. Almabrok Essa, Paheding Sidike, Daniel P Prince, and Vijayan K. Asari, “Automated Oil/Gas Leak Detection System,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
13. Daniel P Prince, Paheding Sidike, Almabrok Essa, and Vijayan K. Asari, “Automatic Building Detection in Wide Area Imagery,” Brother Joseph W Stander Symposium 2016, University of Dayton, Dayton, OH, USA, 20 April 2016.
12. Vijayan K. Asari, Paheding Sidike, Almabrok Essa, and Daniel Prince, “Automated wide area surveillance system for oil/gas pipeline infrastructure protection,” 2016 IEEE/ACM 11th Annual Information Technology Professional Conference at TCF - ITPC 2016, The College of New Jersey, Ewing Township, NJ, USA, March 18, 2016. (Invited Talk)
11. Paheding Sidike, Fatema Albalooshi, Yakov Diskin, ALmabrok Essa and Vijayan K. Asari, “Automatic building change detection by 2D and 3D representation for wide area surveillance,” Brother Joseph W Stander Symposium, , Dayton, OH, USA, April 2015.
10. Paheding Sidike, ALmabrok Essa, and Vijayan K. Asari, “Automatic intrusion detection on pipeline right-of-way via aerial imagery,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, April 2015.
9. Fatema Albalooshi, Paheding Sidike, Yakov Diskin, and Vijayan K. Asari, “A self-organizing maps approach to segmenting tumors in computed tomography (CAT) and magnetic resonance imaging (MRI) scans,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, April 2015.
8. Evan Krieger, Paheding Sidike, Theus Aspiras, and Vijayan K. Asari, “Directional ringlet intensity feature transform for pedestrian tracking,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, April 2015.
7. Paheding Sidike and Vijayan K. Asari, “Rotation, scaling and illumination invariant pattern recognition using joint transform correlation for object detection and tracking,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, April 2015.
6. Paheding Sidike and Vijayan K. Asari, “Detection of Machinery Threat on Pipeline Right-of-Way,” 2014 Pipeline Research Council International (PRCI) Research Exchange Meeting, Atlanta, GA, USA, 04-06 February 2014.
5. Paheding Sidike, Chen Cui, Binu M Nair, Sai B Arigela, Yakov Diskin and Vijayan K. Asari, “Advanced Image Processing for Automatic Pipeline Right-Of-Way Threat Detection,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, 9 April 2014.
4. Paheding Sidike, Sai B Arigela, Yakov Diskin and Vijayan K. Asari, “Visibility Improvement through Hyperspectral Band Integration,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, 9 April 2014.
3. Andrew D Braun, Chen Cui, Paheding Sidike, Solomon G Duning, Binu M Nair, Theus H Aspiras, Yakov Diskin and Vijayan K. Asari, “An Interactive Robust Artificial Intelligence-based Defense Electro Robot (RAIDER) using a Pan-Tilt-Zoom Camera,” Brother Joseph W Stander Symposium, University of Dayton, Dayton, OH, USA, 9 April 2014.

Thesis & Dissertation
2. Paheding Sidike, “Progressively Expended Neural Network for Automatic Material Identification in Hyperspectral Imagery," University of Dayton, Ph.D. in Electrical Engineering, Dayton, Ohio, USA, December 2016.
1. Paheding Sidike, “Techniques for Detection and Identification of Surface and Subsurface Oil Via Hyperspectral Imaging," University of South Alabama, Master of Science in Electrical Engineering, Mobile, AL, USA, May 2013.

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