The lab focuses on advancing research in machine learning, computer vision and big data analytics for remote sensing applications to address the state-of-the-art technical and application challenges.
Rapid advances in sensor technology, field robotics, unmanned aerial systems (UAS), and computing power have facilitated exponential growth in remote sensing applications. Meanwhile, processing complex, multiscale, and multidimensional data from UAS, satellites, environmental sensors and climate model simulations has become increasingly difficult for both scientists and public to summarize and visualize the large amount of data for agricultural and environmental assessments with direct applications to education, training and decision-making. Data generated by thousands of images collected in a single UAS flight is almost impossible to manually analyze. There exist both numerous opportunities and challenges with broader usage of multi-sensor remote sensing data, mostly attributed to processing algorithms of ultra-high resolution imagery, translating the abundant spectral and spatial data to information useful for decision-making. Machine Learning/AI, as a multidisciplinary field, seeks to mimic human brain system for automatic extraction, analysis, and interpreting useful information from images or a sequence of video frames.
Selected Publications
[14]. Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., Fritschi, F. (2019). Unmanned Aerial System (UAS)-based crop yield prediction using multi-sensor data fusion and deep neural network. (under review).
[13]. Peterson, K.T., Sagan, V., John Sloan. (2019). Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. (under review).
[12]. Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., Mockler, T., Fritschi, F. (2019). dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 imagery. Remote Sensing of Environment, 221: 756-772.
[11]. Maimaitiyiming, M., Sagan, V., Sidike, P., Kwasniewski, M. (2019). Dual activation function based Extreme Learning Machine (ELM) for estimating grapevine berry yield and quality. Remote Sensing, 11(7), 740; doi: 10.3390/rs11070740
[10]. Hartling, H., Sagan, V., Sidike, P., Maimaitijiang, M., Carron, J. (2019). Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19(6), 1284; doi: 10.3390/s19061284.
[09]. Peterson, K.T., Sagan, V., Sidike, P., Hasenmueller, E., Sloan, J., Knouft, J. (2019). Machine learning based ensemble prediction of water quality variables with proximal remote sensing using feature-level and decision-level fusion. Photogrammetric Engineering and Remote Sensing, 85(4): 269–280.
[08]. Sidike, P., Asari, V., Sagan, V. (2018). 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, 146: 161-181.
[07]. Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., Martinez, M. (2018). Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine. Remote Sens., 10(10), 1503.
[06]. Nurmemet, I., Sagan, V., Ding, J-L., Halik, Ü., Abliz, A., Yakup, Z. (2018). A WFS-SVM model for Soil Salinity Mapping in Keriya Oasis, NW China using polarimetric decomposition and fully PolSAR data. Remote Sens., 10(4), 598; doi:10.3390/rs10040598.
[05]. Albalooshi, F., Sidike, P., Sagan, V., Albastaki Y., and Asari, V. (2018). Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Aerial Imagery. Photogrammetric Engineering and Remote Sensing, 84(7): 451-458.
[04]. Ding, J., Yang, A., Wang, J., Sagan, V. and Yu, D. (2018). Machine learning based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6: e5714, doi: 10.7717/peerj.5714
[03]. Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Peterson, J., Kadam, S., Burken, J., Fritschi, F. (2017). Unmanned aerial system-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134:43-58.
[02]. Sidike, P., Ghulam, A., Asari, V.K., and Alam, M.S. (2017). Efficient hyperspectral target detection using class-associative spectral fringe-adjusted JTC with dimensionality reduction techniques. Asian Journal of Physics, 26(3&4): 171:180.
[01]. Nurmemet, I., Ghulam, A., Tiyip, T., et al. (2015). Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive reflective and Active Microwave Remote Sensing Data. Remote Sensing, 7(7): 8803-8829.
[13]. Peterson, K.T., Sagan, V., John Sloan. (2019). Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. (under review).
[12]. Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., Mockler, T., Fritschi, F. (2019). dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 imagery. Remote Sensing of Environment, 221: 756-772.
[11]. Maimaitiyiming, M., Sagan, V., Sidike, P., Kwasniewski, M. (2019). Dual activation function based Extreme Learning Machine (ELM) for estimating grapevine berry yield and quality. Remote Sensing, 11(7), 740; doi: 10.3390/rs11070740
[10]. Hartling, H., Sagan, V., Sidike, P., Maimaitijiang, M., Carron, J. (2019). Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19(6), 1284; doi: 10.3390/s19061284.
[09]. Peterson, K.T., Sagan, V., Sidike, P., Hasenmueller, E., Sloan, J., Knouft, J. (2019). Machine learning based ensemble prediction of water quality variables with proximal remote sensing using feature-level and decision-level fusion. Photogrammetric Engineering and Remote Sensing, 85(4): 269–280.
[08]. Sidike, P., Asari, V., Sagan, V. (2018). 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, 146: 161-181.
[07]. Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., Martinez, M. (2018). Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine. Remote Sens., 10(10), 1503.
[06]. Nurmemet, I., Sagan, V., Ding, J-L., Halik, Ü., Abliz, A., Yakup, Z. (2018). A WFS-SVM model for Soil Salinity Mapping in Keriya Oasis, NW China using polarimetric decomposition and fully PolSAR data. Remote Sens., 10(4), 598; doi:10.3390/rs10040598.
[05]. Albalooshi, F., Sidike, P., Sagan, V., Albastaki Y., and Asari, V. (2018). Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Aerial Imagery. Photogrammetric Engineering and Remote Sensing, 84(7): 451-458.
[04]. Ding, J., Yang, A., Wang, J., Sagan, V. and Yu, D. (2018). Machine learning based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6: e5714, doi: 10.7717/peerj.5714
[03]. Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Peterson, J., Kadam, S., Burken, J., Fritschi, F. (2017). Unmanned aerial system-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134:43-58.
[02]. Sidike, P., Ghulam, A., Asari, V.K., and Alam, M.S. (2017). Efficient hyperspectral target detection using class-associative spectral fringe-adjusted JTC with dimensionality reduction techniques. Asian Journal of Physics, 26(3&4): 171:180.
[01]. Nurmemet, I., Ghulam, A., Tiyip, T., et al. (2015). Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive reflective and Active Microwave Remote Sensing Data. Remote Sensing, 7(7): 8803-8829.
