IEEE Geoscience and Remote Sensing Magazine - December 2023 - 94
Real-Time Semantic
Segmentation
A brief survey and comparative
study in remote sensing
CLIFFORD BRONI-BEDIAKO , JUNSHI XIA ,
AND NAOTO YOKOYA
R
eal-time semantic segmentation of remote sensing
imagery is a challenging task that requires a
tradeoff 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 (DNNs)] for real-time semantic
segmentation in computer vision, researchers
have adopted these efficient DNNs in remote
sensing image analysis. This article begins with a
summary of the fundamental compression methods
for designing efficient DNNs 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 DNNs on a
publicly available remote sensing semantic segmentation
benchmark dataset, OpenEarthMap. The experimental results
of an extensive comparative study demonstrate that
most of the existing efficient DNNs have good segmentation
quality, but they suffer low inference speed (i.e.,
a 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.
Digital Object Identifier 10.1109/MGRS.2023.3321258
Date of current version: 24 October 2023
94 2473-2397/23©2023IEEE
INTRODUCTION
Semantic segmentation is a problem of labeling each pixel
in an image with a class label to partition the image into semantically
meaningful segments. It is a pixel-level classification
problem as compared to image classification, which
assigns a class label to the entire image [1]. In recent years,
efficient DNNs for real-time semantic segmentation have
held a great position in the computer vision community [2],
[3], [4], [5], [6]. Efficient DNNs are neural networks with
low computational footprints and inference time [7]. The
success of these methods has made a considerable impact in
real-time applications in the fields of autonomous vehicles
[8], [9], robot vision [10], [11], and medical image analysis
[12], [13], which require high-end computer vision systems.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2023
https://orcid.org/0000-0002-5808-3801
https://orcid.org/0000-0002-5586-6536
https://orcid.org/0000-0002-7321-4590
IEEE Geoscience and Remote Sensing Magazine - December 2023
Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2023
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