Patil, Pratik2025-07-222025-07-222025-0633p.http://hdl.handle.net/10263/7595Dissertation under the supervision of Dr. Sarbani PalitSynthetic Aperture Radar (SAR) technology offers a robust solution for monitoring glacier surface motion, particularly in regions with challenging environmental conditions, since it do not dependence on time of day and weather. This paper presents an enhanced glacier motion monitoring approach based on a Deep Matching Network (DMN), which learns patch-pair correspondences in an end-to-end manner. Unlike traditional shallow feature tracking methods, DMN utilize deep feature similarity through a Siamese network architecture with dense connection blocks to maximize feature reuse and improve training efficiency. To further improve precision and reduce computational cost, the proposed method uses a variable search window and adaptive patch sizing, enabling efficient and accurate motion estimation across diverse glacier terrains. Experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy and efficiency in glacier motion tracking on SAR data.enDeep Matching Network (DMN)Gaussian PyramidDigital elevation model (DEM)Template MatchingGlacier surface motionDense connectionCBAMOther