# Multi-Temporal Aerial Image Registration Using Semantic Features

Source: [Multi-Temporal Aerial Image Registration Using Semantic Features](<https://www.sota2.com/research/paper/68fa66651e209131cea1017d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:49:28.917Z

Authors: Ananya Gupta, Yao Peng, Simon Watson, Hujun Yin

Published: 2019-08-30T16:20:28.000Z

## Abstract

A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.11822>)

[PDF](<https://arxiv.org/pdf/1908.11822.pdf>)