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Title Feature Point based Image Registration between Satellite and Aerial Images of Agricultural Land

Author(s) Mohsin Abbas

Abstract Image Registration is a process of geometrically aligning two images (reference and target) of the same scene, taken from different viewpoints, at different times or by different sensors. Image registration is used in a wide range of remote sensing applications. The rapid advancement in remote sensing sensors has drastically increase the use of Satellite Imagery (SI) and Unmanned Aerial Vehicle (UAV) images in different applications such as traffic monitoring, agriculture land analysis, early warning systems and damage assessment. This thesis focuses on an agricultural application of SI and UAV images. The SI are low resolution images as they are captured from very high altitude, whereas, the UAV images are taken from low flying platform, have high resolution and relatively good quality. But the UAV images lack geo-referencing and cannot be used directly in remote sensing applications. This problem in literature is dealt with feature point based geo-registration between SI-UAV images. In case of agricultural SI-UAV images, the registration process is a challenging task. This is due to temporal nature of agricultural crops, which results in high textural and intensity differences between the SI-UAV images. Existing feature points such as Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB), are not invariant to temporal, textural and intensity differences and underperform in the image registration task. This thesis proposes a new method that combines the strength of Nearest Neighbor (NN) and Brute Force (BF) descriptor matching strategies to register SI-UAV images. The proposed method is named as NNBF method. In the proposed NN-BF method, the corresponding feature point matches are first identified between SI-UAV images of the training set with overlap error. Then the corresponding feature point matches are used with NN and BF based descriptor matching strategies to register the SI-UAV images of the test set. Experiments are performed on SI-UAV image dataset of agricultural land. The experimental results show that NN-BF improves the matching and precision scores of SIFT by 20.4% and 32% respectively. Whereas in case of SURF, the NN-BF method improves the matching and precision scores by 19.5% and 21.8% respectively. Keywords: Agriculture land, Feature point detectors, Feature point descriptors, Image registration, Satellite imagery, UAV images.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2019-05-08

Subject Computer Science, Image Processing

Publisher NUML

Contributor(s) Mohsin Abbas and Dr. Sajid Saleem

Format PDF

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Source

Relation

Coverage

Rights NUML

Category MSCS Thesis

Description MS Thesis by Mohsin Abbas and Dr. Sajid Saleem