Remote Sensing

Projects related to Remote Sensing (Satellite/UAV):

Satellite Remote Sensing:

Phenology modelling

To conduct research in the area of land surface phenology. These duties will include gathering, processing and analyzing Satellite/UAV/Near-surface remote sensing data and also developing mathematical model and computing codes. The phenology modelling is performed using spatio-temporal analysis with the satellite data and validated using near-surface vegetation indices. This information is to understand climate change and also in agriculture planning.

Phenology detected for time-series VIIRS satellite data Validation of phenology in a forest region using near-surface remote sensing data

Related publications

1. Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath, “Integrated phenology and climate in rice yields prediction using machine learning methods”, Ecological Indicators, Elsevier, Vol. 120, 106935, 2021

2. X. Zhang, J. Senthilnath, L. Liu, M.A. Friedl, G.M. Henebry, Y. Liu, C. Schaaf, A. Richardson, J.M. Gray, “Evaluation of Land Surface Phenology from VIIRS Data using Time Series of PhenoCam Imagery”, Agriculture and Forest Meteorology, Elsevier, Vol. 256-257, pp. 137 – 149, 2018

3. X. Zhang, L. Liu, Y. Liu, J. Senthilnath, J. Wang, M. Moon, G.M. Henebry, M.A. Friedl, C. Schaaf, “Generation and Evaluation of the VIIRS Land Surface Phenology Product", Remote Sensing of Environment, Elsevier, Vol. 216, pp. 212 – 229, 2018

4. X. Zhang, J. Wang, F. Gao, Y. Liu, C. Schaaf, M.A. Friedl, Y. Yu, J. Senthilnath, J.M. Gray, L. Liu, D. Yan, G.M. Henebry, “Exploration of scaling effects on coarse resolution land surface phenology", Remote Sensing of Environment, Elsevier, Vol. 190, pp. 318 – 330, 2017

5. X. Zhang, J. Senthilnath, J. Wang, G.M. Henebry, J.M. Gray, M.A. Friedl, Y. Liu, C. Schaaf, A. Shuai, “Validation of VIIRS Land Surface Phenology using Field Observations, PhenoCam Imagery, and Landsat data”, Fall Meeting, AGU, USA, 2016

6. X. Zhang, M.A. Friedl, G.M. Henebry, J. Senthilnath, J.M. Gray, Y. Liu, C. Schaaf, J. Wang, “Validation of VIIRS Land Surface Phenology Using PhenoCam and Landsat Data”, NASA MODIS/VIIRS Science Team Meeting, USA, 2016

7. X. Zhang, M.A. Friedl, G.M. Henebry, J. Senthilnath, J.M. Gray, Y. Liu, C. Schaaf, J. Wang, “Development and Validation of a Land Surface Phenology Product from VIIRS”, NASA MODIS/VIIRS Science Team Meeting, USA, 2016

8. X. Zhang, M.A. Friedl, G.M. Henebry, J. Senthilnath, J.M. Gray, C. Schaaf, Y. Liu, “Comparison of Land Surface Phenology Retrieved from MODIS and VIIRS Data”, NASA MODIS/VIIRS Science Team Meeting, USA, 2015

Disaster monitoring

The project aims at the design of automatic systems dedicated to develop a satellite image processing framework for precise assessment of damages caused by the flood. In recent years, remote sensing technology along with Geographic Information System (GIS) has become the key tool for flood monitoring, particularly in providing a synoptic vision over a wide area in a short time and in a very cost-effective manner. However, as regards remote sensing applications at the local scale, researchers have not yet proved in a completely satisfactory way the competitiveness of satellite-based methods compared with ground measures and aerial surveys. The automatic image processing system can be evolved as part of a Geographic Information System (GIS) for the flood-prone regions. The system integrates the structural and spatial knowledge, texture and spectral information with the techniques of image fusion, image classification, numerical transforms and boundary extraction. Results are given in terms of the extent of inundated surface and type of flooded area. This information can be used to estimate property damage, in the context of decision-makers and insurance companies. This process of maintenance is an ongoing process and automating it will reduce the overall effort in the long run.

a) Case study 1: Krishna river basin

Before flood - MODIS image During flood - MODIS image After flood - MODIS image

b) Case study 2: Kosi river basin

Before flood - LISS III image

During flood - SAR image

Land cover mapping of flooded and unflooded regions

Related publications

1. Yahui Guo, Shunqiang Hu, Wenxiang Wu, Yuyi Wang, J. Senthilnath, “Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China", Environmental Monitoring and Assessment, Springer, Vol. 192(7):464, pp. 1 - 16, 2020

2. J. Senthilnath, Shreyas P.B, Ritwik Rajendra, S. Suresh, Sushant Kulkarni, J.A. Benediktsson, “Hierarchical clustering approaches for flood assessment using multi-sensor satellite images”, International Journal of Image and Data Fusion, Taylor & Francis, Vol. 10(1), pp. 28 - 44, 2019

3. C.S. Arvind, Ashoka Vanjare, S.N. Omkar, J. Senthilnath, V. Mani, P.G. Diwakar, “Flood Assessment using Multi-temporal MODIS Satellite Images”, Proceedings of the Twelfth International Conference on Image and Signal Processing (ICISP'16), India, 2016 [This research paper was conferred the Best Paper Award]

4. J. Senthilnath and X-S. Yang, “Multitemporal remote sensing image classification by nature-inspired techniques”, Bio-Inspired Computation and Applications in Image Processing, Elsevier, Chapter 9, pp. 187-219, 2016

5. J. Senthilnath, Ram Prasad, Ritwik Rajendra, Shreyas P.B, S. N. Omkar, V. Mani, “Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence”, Proceedings of the IEEE International Conference on Cognitive Computing and Information Processing (CCIP'15), India, 2015

6. J. Senthilnath, Naveen P. Kalro, J.A. Benediktsson, “Accurate Point Matching Based on Multi-objective Genetic Algorithm for Multi-sensor Satellite Imagery”, Applied Mathematics and Computation, Elsevier, Vol. 236, pp. 546 - 564, 2014

7. J. Senthilnath, S.N. Omkar, V. Mani, Ashoka Vanjare, P.G. Diwakar, “Multi-Temporal Satellite Image Analysis Using Gene Expression Programming”, Advances in Intelligent Systems and Computing, Springer, Vol. 236, pp. 1039 – 1045, 2014

8. J. Senthilnath, Ram Prasad, “A new SIFT matching criteria in a genetic algorithm framework for registering multisensory satellite imagery”, Proceedings of the ACM 9th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'14), 2014

9. J. Senthilnath, X-S. Yang, J.A. Benediktsson, “Automatic Registration of Multi-Temporal Remote Sensing Images Based on Nature Inspired Techniques”, International Journal of Image and Data Fusion, Taylor & Francis, Vol. 5(4), pp. 263 - 284, 2014

10. J. Senthilnath, Vikram Shenoy H, Ritwik Rajendra, S.N. Omkar, V. Mani, P.G. Diwakar, “Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction”, Journal of Earth System Science, Springer, Vol. 122(3), pp. 559 - 572, 2013

11. J. Senthilnath, S.N. Omkar, V. Mani, P.G. Diwakar, “Multi-temporal Satellite Imagery for Flood Damage Assessment”, Journal of the Indian Institute of Science, Springer, Vol. 93(1), pp. 105 - 116, 2013

12. J. Senthilnath, Vikram Shenoy H, S.N. Omkar, V. Mani, “Spectral-spatial MODIS image analysis using Swarm intelligence algorithms and region based segmentation for flood assessment”, Advances in Intelligent Systems and Computing, Springer, Vol. 202, pp. 163 – 174, 2013

13. C.S. Arvind, Ashoka Vanjare, S.N. Omkar, J. Senthilnath, V. Mani, P.G. Diwakar, “Multi-temporal Satellite Image Analysis Using Unsupervised Techniques”, Advances in Intelligent Systems and Computing, Springer, Vol. 177, pp. 757 – 765, 2013

14. J. Senthilnath, S.N. Omkar, V. Mani, Naveen P Kalro, P.G. Diwakar, “Multi-objective genetic algorithm for efficient point matching in multi-sensor satellite image”, Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS'12), 2012

15. J. Senthilnath, Shivesh Bajpai, S.N. Omkar, P.G. Diwakar, V. Mani, “An approach to multi-temporal MODIS image analysis using image classification and segmentation”, Advances in Space Research, Elsevier, Vol. 50(9), pp. 1274 - 1287, 2012

16. J. Senthilnath, Shreyas P.B, Ritwik Rajendra, S.N. Omkar, V. Mani, P.G. Diwakar, “Multi-Sensor Satellite Image Analysis using Niche Genetic Algorithm for Flood Assessment”, Swarm, Evolutionary and Memetic Computing, Lecture Notes in Computer Science, Springer, Vol. 7677, pp. 49 - 56, 2012

17. S.N. Omkar, J. Senthilnath, K. Abhinandan, Vineeth V. Acharya, P. G. Diwakar, “Assessment of flood-prone region using high-resolution satellite images”, Proc. XXVIII INCA International Congress on Collaborative Mapping and Space Technology, India, 2008 [This research paper was conferred the Best Presentation Award]

Hyperspectral and multispectral image analysis for agriculture data

Design of automatic systems dedicated to analyzing and processing hyperspectral and multispectral data which involves feature extraction, band selection/reduction, etc. Extracting the extent of the crop in a field is an example of feature extraction. By taking into account changes in reflectance, crop extent and crop type can be found out. Image classification forms the core of the solution to the crop coverage identification problem. This information can be used to audit crop usage, in the context of agriculture planning and land-use.

EO-1 Hyperion image Effect of reflectance for a selected pixel of three crop stages

Crop type classification – QuickBird Multi-spectral satellite image

Related publications

1. Sushant Kulkarni, J. Senthilnath, J.A. Benediktsson, “Classification of Multi-Spectral Satellite Image Using Hierarchical Clustering Algorithms”, Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'18), India, 2018

2. Akanksha Dokania, Neelanshi Varia, J. Senthilnath, “EObjCount: An Evolving Spectral and Spatial Approach for Tree Count using Multispectral Satellite Images”, Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'18), India, 2018

3. J. Senthilnath, Nitin Karnwal, D. Sai Teja, "Crop type classification based on clonal selection algorithm for high resolution satellite image", International Journal of Image, Graphics and Signal Processing, MECS, Vol. 6(9), pp. 11 – 19, 2014

4. J. Senthilnath, S.N. Omkar, V. Mani, Nitin Karnwal, Shreyas P.B, "Crop Stage Classification of Hyperspectral Data using Unsupervised Techniques", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6(2), pp. 861 - 866, 2013

5. S.N. Omkar, Nikhil Ramaswamy, J. Senthilnath, Bharath. S, Anuradha N. S., “Gene Expression Programming-Fuzzy Logic Method For Crop Type Classification”, Proceedings of the Sixth International conference on Genetic and Evolutionary Computing (ICGEC'12), 2012

6. J. Senthilnath, S.N. Omkar, V. Mani, Nitin Karnwal, “Hierarchical artificial immune system for crop stage classification”, Proceedings of the IEEE INDICON'11, 2011

7. S. N. Omkar, Sivaranjani V, J. Senthilnath, Suman Mukherjee, “Dimensionality Reduction and Classification of Hyperspectral Data”, International Journal of Aerospace Innovations, Vol. 2(3), pp. 157-163, 2010

8. S.N. Omkar, J. Senthilnath, Dheevatsa Mudigere, M. Manoj Kumar, “Crop classification using biologically inspired techniques with high resolution satellite image”, Journal of the Indian Society of Remote Sensing, Springer, Vol. 36(2), pp. 172 - 182, 2008

Land cover mapping

The project aims at the design of automatic systems dedicated to satellite image classification. The automatic classification system integrates the structural knowledge (shape and spatial relation between the regions) into the classification process. In this project, we generate a multi-source and multi-model Level-II classifier for urban region. This information can be used to audit land usage, in the context of land use and city planning.

Urban region classification - QuickBird Multi-spectral satellite image

Related publications

1. Kunal Kumar Rai, Aparna Rai, Kanishka Dhar, J. Senthilnath, S.N. Omkar, Ramesh K.N., “SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images", Journal of the Indian Society of Remote Sensing, Springer, Vol. 45(1), pp. 55 - 65, 2017

2. J. Senthilnath, Sushant Kulkarni, J.A. Benediktsson, X-S. Yang, “A Novel Approach for Multi-Spectral Satellite Image Classification based on the Bat Algorithm”, IEEE Geoscience and Remote Sensing Letters, Vol. 13(4), pp. 599 - 603, 2016

3. J. Senthilnath, Ankur Raj, S.N. Omkar, V. Mani, Deepak Kumar, “Quasi-Based Hierarchical Clustering for Land Cover Mapping Using Satellite Images”, Advances in Intelligent Systems and Computing, Springer, Vol. 202, pp. 53 – 64, 2013

4. J. Senthilnath, S.N. Omkar, V. Mani, Tejovanth N, P.G. Diwakar, Archana Shenoy B, “Hierarchical Clustering Algorithm for Land Cover Mapping using Satellite Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5(3), pp. 762 - 768, 2012

5. J. Senthilnath, S.N. Omkar, V. Mani, T. Karthikeyan, “Multi-objective optimization of satellite image registration using discrete particle swarm optimisation”, Proceedings of the IEEE INDICON'11, 2011

6. J. Senthilnath, S.N. Omkar, V. Mani, Tejovanth N, P.G. Diwakar, Archana Shenoy B, “Multi-Spectral Satellite Image Classification using Glowworm Swarm Optimization”, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS'11), 2011

Development of a generalized road extraction system

The project aims at the design of a road extraction system using satellite images. The system integrates the structural and spatial knowledge, texture and spectral information with the techniques image fusion, image classification and numerical transforms. In this project, we generate a system to extract roads using multiple satellite images. The road extraction information can be used to make road maps, plan new roads and maintain existing ones. The extracted roads and the information on the condition and the quality of roads can together help the planners and the administration to know the road segments that need maintenance. This process of maintenance is an ongoing process and automating it will reduce the overall effort in the long run.

Road Extraction - QuickBird panchromatic satellite image

Related publications

1. J. Senthilnath, S. Sindhu, S.N. Omkar, “GPU-based normalized cuts for road extraction using satellite imagery", Journal of Earth System Science, Springer, Vol. 123(8), pp. 1759 - 1769, 2014

2. M. Rajeswari, K.S. Gurumurthy, S.N. Omkar, J. Senthilnath, L. Pratap Reddy, ”Automatic Road Extraction using High Resolution Satellite Images based on Level Set and Mean Shift Methods”, Proceedings of the IEEE 3rd International Conference on Electronics Computer Technology (ICECT'11), India, 2011

3. J. Senthilnath, M. Rajeswari, S.N. Omkar, “Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method”, Journal of the Indian Society of Remote Sensing, Springer, Vol. 37(3), pp. 351 - 361, 2009 [This research paper was conferred the Best Paper Award]

4. S.N. Omkar, J. Senthilnath, M. Rajeswari, “Automatic road extraction using high-resolution satellite images based on level set evolution without re-initialization”, Proceedings of the XXVIII INCA International Congress on Collaborative Mapping and Space Technology, 2008

Urban sprawl analysis

Design of automatic systems dedicated to analyzing and processing multitemporal data which involves change detection to discriminate areas of land cover change between dates of imaging. The type of changes that might be of interest can range from short term phenomena such as flood water, crop growth etc., to long term phenomena such as urban fringe development or desertification etc.. Ideally, the automatic image processing system can be evolved as part of a Geographic Information System (GIS) for the change detection. The system integrates the structural and spatial knowledge, texture and spectral information with the techniques of multi-model image registration, classification and analysis using multi-temporal data. Results are given in such terms as to delineate change in multidate imagery using change-versus-no-change information to guide multidate data analysis. This information can be used to estimate various environmental factors that might change between image dates. In addition to atmospheric effects, factors such as water, wind or soil moisture condition might also be important. The other important objective of the proposed project is to do spatio-temporal analysis of multi-time image data to bring out significant changes in the Natural Resources and environment through automated image processing methods.

Urban sprawl - Case study: Bangalore city

Related publications

1. A. Vanjare, S.N. Omkar, J. Senthilnath, "Satellite Image Processing for Land Use and Land Cover Mapping", International Journal of Image, Graphics and Signal Processing, MECS, Vol. 6(10), pp. 18 – 28, 2014

UAV Remote Sensing:

Vegetation analysis

The project aims to build a learning system for crop monitoring and tree crown mapping using VTOL multi-rotor quadcopter UAV and fixed-wing UAV respectively. The main objective is to design a system based on knowledge representation and learning techniques using unsupervised learning.

Multi-rotor quadcopter aircraft Image acquired from quadcopter MLapproach for detection of tomatoes

Fixed-wing aircraft Image acquired from fixed-wing ML approach for tree crown mapping

Related Publications

1. Y. Guo, S. Chen, Z. Wu, S. Wang, C.R. Bryant, J. Senthilnath, M. Cunha, and Y.H. Fu, “Integrating spectral and textural information for monitoring the growth of pear trees using optical images from the UAV platform,” Remote Sensing, MDPI, Vol. 13(2), 1795, 2021

2. Y. Guo, G. Yin, H. Sun, H. Wang, S. Chen, J. Senthilnath, J. Wang, Y. Fu, “Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods”, Sensors, MDPI, Vol. 20(18), 5130, 2020

3. Y. Guo, H. Wang, Z. Wu, S. Wang, H. Sun, J. Senthilnath, J. Wang, C.R. Bryant, Y. Fu, “Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV”, Sensors, MDPI, Vol. 20(18), 5055, 2020

4. Y. Guo, J. Senthilnath, W. Wu, X. Zhang, Z. Zeng, H. Huang, “Radiometric calibration for multispectral camera of different imaging conditions mounted on a UAV platform", Sustainability, MDPI, Vol. 11(4), 978, 2019

5. J. Senthilnath, M. Kandukuri, A. Dokania, K.N. Ramesh, “Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods”, Computers and Electronics in Agriculture, Elsevier, Vol. 140, pp. 8 - 24, 2017

6. J. Senthilnath, A. Dokania, M. Kandukuri, K.N. Ramesh, G. Anand, S.N. Omkar, “Detection of Tomatoes using Spectral-Spatial methods in Remotely Sensed RGB Images Captured by UAV”, Biosystems Engineering, Elsevier, Vol. 146, pp. 16 - 32, 2016

Power line monitoring

The project aims to build a learning system for power line monitoring using VTOL multi-rotor quadcopter UAV. The main objective is to detect the power line using supervised and unsupervised learning.

Multi-rotor quadcopter aircraft Image acquired from quadcopter aircraft ML approach for power line monitoring

Related Publications

1. J. Senthilnath, A. Kumar, A. Jain, K. Harikumar, M. Thapa, S. Suresh, G. Anand, J.A. Benediktsson, “BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery", IEEE Transactions on Geoscience and Remote Sensing (Accepted)

2. R. Bhola, N.H. Krishna, K.N. Ramesh, J. Senthilnath, Gautham Anand, “Detection of the power lines in UAV remote sensed images using spectral-spatial methods", Journal of Environmental Management, Elsevier, Vol. 206, pp. 1233 - 1242, 2018

3. S.N. Omkar, J. Senthilnath, K.N. Ramesh, A. Jain, A. Kumar, G. Anand, “Detection of Powerlines in Aerial Images”, Patent no: WO201821139A1, 2018.

4. K.N. Ramesh, A.S. Murthy, J. Senthilnath, S.N. Omkar, “Automatic detection of power lines in UAV remote sensed images”, Proceedings of the IEEE 2nd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON’15), 2015

Generalized road extraction system

The project aims at the design of a road extraction system using UAV images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset.

Fixed-wing aircraft Image acquired from fixed-wing aircraft DL approach for road extraction

Related publications

1. J. Senthilnath, N. Varia, A. Dokania, G. Anand, J.A. Benediktsson, “Deep TEC: Deep Transfer learning with Ensemble Classifier for Road Extraction from UAV Imagery”, Remote Sensing, MDPI, Vol. 12(2), 245, 2020 [This research paper was conferred as one of the feature paper among 5 out of 140 papers]

2. N. Varia, A. Dokania, J. Senthilnath, “DeepExt: A Convolution Neural Network for Road Extraction using RGB images captured by UAV”, Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'18), 2018

Page updated

Google Sites

Report abuse