Clutter Classification Using Deep Learning in Multiple Stages

TL;DR

This paper presents a deep learning approach to automatically classify environmental clutter types from satellite imagery to improve wireless path loss prediction accuracy.

Contribution

It introduces a novel deep learning method for clutter classification in satellite images, enhancing propagation models for wireless communication.

Findings

Deep learning accurately classifies clutter types

Improved path loss prediction models

Enhanced environmental awareness for wireless planning

Abstract

Path loss prediction for wireless communications is highly dependent on the local environment. Propagation models including clutter information have been shown to significantly increase model accuracy. This paper explores the application of deep learning to satellite imagery to identify environmental clutter types automatically. Recognizing these clutter types has numerous uses, but our main application is to use clutter information to enhance propagation prediction models. Knowing the type of obstruction (tree, building, and further classifications) can improve the prediction accuracy of key propagation metrics such as path loss.