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 : علمی - پژوهشی


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


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.



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