Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing

TL;DR

This survey reviews recent efficient deep learning methods for real-time semantic segmentation in remote sensing, highlighting their strengths and limitations, especially in inference speed, and discusses future research directions.

Contribution

It provides a comprehensive survey of recent methods, a taxonomy based on architecture design, and an extensive comparative evaluation on a benchmark dataset.

Findings

Most methods have good segmentation quality

Existing methods suffer from low inference speed

Speed limitations may hinder real-time deployment

Abstract

Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off between effectiveness and efficiency. It has many applications including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. With the success of efficient deep learning methods (i.e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis. This paper begins with a summary of the fundamental compression methods for designing efficient deep neural networks and provides a brief but comprehensive survey, outlining the recent developments in real-time semantic segmentation of remote sensing imagery. We examine several seminal efficient deep learning methods, placing them in a taxonomy based on the network architecture design approach. Furthermore, we evaluate the quality and efficiency of some existing efficient deep neural networks on a publicly available remote sensing semantic segmentation benchmark dataset, the OpenEarthMap. The experimental results of an extensive comparative study demonstrate that most of the existing efficient deep neural networks have good segmentation quality, but they suffer low inference speed (i.e., high latency rate), which may limit their capability of deployment in real-time applications of remote sensing image segmentation. We provide some insights into the current trend and future research directions for real-time semantic segmentation of remote sensing imagery.