Edge Detection for Satellite Images without Deep Networks

TL;DR

This paper proposes a novel edge detection method for satellite images that does not rely on deep learning, aiming to reduce computational costs and dependency on training data.

Contribution

The paper introduces a non-deep learning approach for satellite image edge detection, addressing computational and data annotation challenges.

Findings

Effective edge detection without deep networks

Reduces computational resource requirements

Less dependency on training data

Abstract

Satellite imagery is widely used in many application sectors, including agriculture, navigation, and urban planning. Frequently, satellite imagery involves both large numbers of images as well as high pixel counts, making satellite datasets computationally expensive to analyze. Recent approaches to satellite image analysis have largely emphasized deep learning methods. Though extremely powerful, deep learning has some drawbacks, including the requirement of specialized computing hardware and a high reliance on training data. When dealing with large satellite datasets, the cost of both computational resources and training data annotation may be prohibitive.