BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
CoRR(2024)
摘要
Satellites equipped with optical sensors capture high-resolution imagery,
providing valuable insights into various environmental phenomena. In recent
years, there has been a surge of research focused on addressing some challenges
in remote sensing, ranging from water detection in diverse landscapes to the
segmentation of mountainous and terrains. Ongoing investigations goals to
enhance the precision and efficiency of satellite imagery analysis. Especially,
there is a growing emphasis on developing methodologies for accurate water body
detection, snow and clouds, important for environmental monitoring, resource
management, and disaster response. Within this context, this paper focus on the
cloud segmentation from remote sensing imagery. Accurate remote sensing data
analysis can be challenging due to the presence of clouds in optical
sensor-based applications. The quality of resulting products such as
applications and research is directly impacted by cloud detection, which plays
a key role in the remote sensing data processing pipeline. This paper examines
seven cutting-edge semantic segmentation and detection algorithms applied to
clouds identification, conducting a benchmark analysis to evaluate their
architectural approaches and identify the most performing ones. To increase the
model's adaptability, critical elements including the type of imagery and the
amount of spectral bands used during training are analyzed. Additionally, this
research tries to produce machine learning algorithms that can perform cloud
segmentation using only a few spectral bands, including RGB and RGBN-IR
combinations. The model's flexibility for a variety of applications and user
scenarios is assessed by using imagery from Sentinel-2 and Landsat-8 as
datasets. This benchmark can be reproduced using the material from this github
link: .
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