Log In Sign Up

Edge Detection for Satellite Images without Deep Networks

by   Joshua Abraham, et al.

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.


page 2

page 3

page 4

page 5

page 6

page 7


Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning

Object detection and classification for aircraft are the most important ...

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture

Agricultural research is essential for increasing food production to mee...

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

The recent explosion in applications of machine learning to satellite im...

HRPlanes: High Resolution Airplane Dataset for Deep Learning

Airplane detection from satellite imagery is a challenging task due to t...

Workflow Design Analysis for High Resolution Satellite Image Analysis

Ecological sciences are using imagery from a variety of sources to monit...

Generating Interpretable Poverty Maps using Object Detection in Satellite Images

Accurate local-level poverty measurement is an essential task for govern...