Geometric Correction of BaySpec OCI-F Pushbroom Hyperspectral Images via the Sequential Geometric Mappings among Simultaneous Video Frames Estimated by Least Square Matching

Document Type : Original Article

Authors

1 M.Sc. Student in Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran

2 Full Prof. in Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran

3 Assistant Prof. in Photogrammetry and Remote Sensing, Dep. of Geodesy and Surveying Engineering, Tafresh University, Tafresh

Abstract

Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control Points (GCPs). This fact highlights the need for developing geometric corrections methods for any camera design. BaySpec OCI-F (400-1000 nm) is one of the innovative cameras that acquires pushbroom hyperspectral images. In addition to the pushbroom sensor, the camera uses a frame sensor that acquires images at the same time as the pushbroom sensor and with the same temporal rate. In this article, a geometric correction method for pushbroom images of OCI-F camera is proposed. Based on the camera’s imaging design, the first step of the method determines a set of calibration parameters which geometrically relates the pushbroom and the frame sensors. Then using this relation and the geometric relations among consecutive frames, the pixels of the pushbroom scene are rearranged and form the corrected image. The proposed method determines the relation among the consecutive images via Least Square Matching (LSM) method. The results show that the correction method has decreased the geometric distortions of the raw pushbroom scene by 62.2% on average. Such a reduction causes the average accuracies of two-dimensional and three-dimensional generic models which relate image space and ground space together, to increase by 34.1% and 39.9% respectively.

Keywords

References

 

Arroyo-Mora, J.P., Kalacska, M., Inamdar, D., Soffer, R., Lucanus, O., Gorman, J., Naprstek, T., Schaaf, E.S., Ifimov, G. & Elmer, K., 2019, Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring, Drones, 3(1), P. 12, https://doi. org/10. 3390/drones3010012.
Barbarella, M., Fiani, M. & Zollo, C., 2017, Assessment of DEM Derived from very High-Resolution Stereo Satellite Imagery for Geomorphometric Analysis, European Journal of Remote Sensing, 50(1), PP. 534-549, https://doi. org/10.1. 1372084.2017. 22797254/080.
Barbieux, K., 2018, Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and area-Based Co-Registration Techniques, Remote Sensing, 10(4), P. 645.
Barbieux, K., Constantin, D. & Merminod, B., 2016, Correction of Airborne Pushbroom Images Orientation Using Bundle Adjustment of Frame Images, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, https://doi. org/10. 5194/isprs-archives-XLI-B3-813-2016.
BaySpec, 2016, OCI-F Ultra-Compact Hyper-spectral Imager User Manual, https://geo-matching.com/uploads/default/m/i/ migrationuajxbx. pdf.
Cariou, C. & Chehdi, K., 2008, Automatic Georeferencing of Airborne Pushbroom Scanner Images with Missing Ancillary Data Using Mutual Information, IEEE Transactions on Geoscience and Remote Sensing, 46(5), PP. 1290-1300.
 
Fang, H., Hu, B., Yu, Z., Xu, H., He, C., Li, A. & Liu, Y., 2018, Semi-Automatic Geometric Correction of Airborne Hyperspectral Push-Broom Images Using Ground Control Points and Linear Features, International journal of remote sensing, 39(12), PP. 4115-4129, https://doi. org/10. 1080/01431161. 2018. 1455237 .
Guerrero, J. & Sagues, C., 2001, From Lines to Homographies between Uncalibrated Images, IX Symposium on Pattern Re-cognition and Image Analysis, PP. 233-240.
Habib, A.F., Morgan, M.F., Jeong, S. & Kim, K.O., 2005, Epipolar Geometry of Line Cameras Moving with Constant Velocity and Attitude, ETRI Journal, 27(2), PP. 172-180, https://doi. org/10. 4218/etrij. 05. 0104. 0086.
Habib, A., Han, Y., Xiong, W., He, F., Zhang, Z. & Crawford, M., 2016a, Automated ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery, Remote Sensing, 8(10), P. 796, https://doi. org/10. 3390/rs8100796.
Habib, A., Xiong, W., He, F., Yang, H.L. & Crawford, M., 2016b, Improving Orthorectification of UAV-Based Push-Broom Scanner Imagery Using Derived Orthophotos from Frame Cameras, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1), PP. 262-276, https://doi. org/10. 1109/JSTARS. 2016. 2520929.
Jannati, M. & Valadan Zoej, M.J., 2015, Introducing Genetic Modification Concept to Optimize Rational Function Models (RFMs) for Georeferencing of Satellite Imagery, GIScience & Remote Sensing, 52(4), PP. 510-525, https://doi. org/10. 1080/15481603. 2015. 1052634.
Jannati, M., Valadan Zoej, M.J. & Mokhtarzade, M., 2017, Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery Using the Rigorous Sensor Model, Sensors, 17(1), P. 129.
Jurado, J.M. Padua, L., Hruska, J., Feito, F.R. & Sousa, J.J., 2021, An Efficient Method for Generating UAV-based Hyperspectral Mosaics Using Push-Broom Sensors, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, https://doi. org/10. 1109/JSTARS. 2021. 3088945.
Lowe, D.G., 2004, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, 60(2), PP. 91-110, https://doi. org/10. 1023/B:VISI. 0000029664. 99615. 94.
OCI-F Series Hyperspectral Cameras, 2016, https://www.optonlaser.com/produit/cameras- hyper-spectrales/OCI-F-Cam%C3%A9ra-Hyperspectrale-VIS-NIR-SWIR. pdf.
Online, P., 2020, Hyperspectral Imager OCItm-F Series (400-1000nm), https://www.photonicsonline.com/doc/ hyperspectral-imagers-oci-f-series-0001.
Orun, A.B. & Natarajan, K., 1994, A Modified Bundle Adjustment Software for SPOT Imagery and Photography- Tradeoff, Photogrammetric Engineering and Remote Sensing, 60(12), PP. 1431-1438, https://www. asprs. org/wp-content/uploads/ pers/1994journal/dec/1994_dec_1431-1437. pdf.
Pöntinen, P., 1999, On the Creation of Panoramic Images from Image Sequences, Photogrammetric Journal of Finland, 16(2), PP. 43-67, https://research. aalto. fi/en/publications/on-the-creation-of-panoramic-images-from-image-sequences.
Ramirez‐Paredes, J.P., Lary, D.J. & Gans, N.R., 2016, Low‐Altitude Terrestrial Spectro-scopy from a Pushbroom Sensor, Journal of Field Robotics, 33(6), PP. 837-852, https://doi. org/10. 1002/rob. 21624.
Safdarinezhad, A., Mokhtarzade, M. & Valadan Zoej, M.J., 2019, An Automatic Method for Precise 3D Registration of High Resolution Satellite Images and Airborne LiDAR Data, International Journal of Remote Sensing, 40(24), PP. 9460-9483, https://doi. org/10. 1080/01431161. 2019. 1633698.
Sedaghat, A. & Ebadi, H., 2015, Accurate Affine Invariant Image Matching Using Oriented Least Square, Photogrammetric Engineering & Remote Sensing, 81(9), PP. 733-743, https://doi. org/10. 14358/PERS. 81. 9. 733.
Suomalainen, J., Anders, N., Iqbal, S., Roerink, G., Franke, J., Wenting, P., Hünniger, D., Bartholomeus, H., Becker, R. & Kooistra, L., 2014, A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles, Remote Sensing, 6(11), P. 11013-11030, https://doi. org/10. 3390/rs61111013 .
Tao, C.V. & Hu, Y., 2001, A Comprehensive Study of the Rational Function Model for Photogrammetric Processing, Photogrammetric Engineering and Remote Sensing, 67(12), PP. 1347-1358, https://www. researchgate. net/publication/ 228902962_A_Comprehensive_study_of_the_rational_function_model_for_photogrammetric_processing.
Tjahjadi, M.E. & Handoko, F., 2017, Precise Wide Baseline Stereo Image Matching for Compact Digital Cameras, 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI).
Yang, N., Zhang, Y. & Li, J., 2020, A Least Square Matching Optimization Method of Low Altitude Remote Sensing Images Based on Self-Adaptive Patch, IOP Conference Series: Earth and Environmental Science.
Zhang, H., Zhang, B., Wei, Z., Wang, C. & Huang, Q., 2020, Lightweight Integrated Solution for a UAV-Borne Hyperspectral Imaging System, Remote Sensing, 12(4), P. 657, https://doi. org/10. 3390/rs12040657.

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