Performance Evaluation of Feature Descriptors for Aerial Imagery Mosaicking

Abstract

Mosaicking enables efficient summary of geospatial content in an aerial video with applications in surveillance, activity detection, tracking, etc. Scene clutter, presence of distractors, parallax, illumination artifacts i.e. shadows, glare, and other complexities of aerial imaging such as large camera motion makes the registration process challenging. Robust feature detection and description is needed to overcome these challenges before registration. This study investigates the computational complexity versus performance of selected feature detectors such as Structure Tensor with NCC (ST+NCC), SURF, ASIFT within our Video Mosaicking and Summarization (VMZ) framework on VIRAT benchmark aerial video. ST+NCC and SURF is very fast but fails for few complex imagery (with occlusion) from VIRAT. ASIFT is more robust compared to ST+NCC or SURF, though extremely time consuming. We also propose an Adaptive Descriptor (combining ST+NCC and ASIFT) that is 9x faster than ASIFT with comparable robustness.