A comparative study of atmospheric and water column correction using various algorithms on Landsat imagery to identify Coral Reefs

Kondraju, Tarun Teja; Mandla, Venkata Ravibabu; Chokkavarapu, Nagaveni; Peddinti, Veerendra Satya Sylesh

doi:10.1016/j.rsma.2021.102082

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Marre [34]. Kondraju et al proposed an atmospheric and water column correction technique to remove the noise for detecting coral-reef condition through remote sensing [35]. Liu et al monitored the mass coral bleaching effects using the thermal stress from sea surface temperature and high spatial-resolution Sentinel-2B remote sensing imagery systems [36].…”

Section: Related Workmentioning

confidence: 99%

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset

Sivamani

Nedumaran

2023

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Coral-reefs are a significant species in marine life, which are affected by multiple diseases due to the stress and variation in heat under the impact of the ocean. The autonomous monitoring and detection of coral health are crucial for researchers to protect it at an early stage. The detection of coral diseases is a difficult task due to the inadequate coral-reef datasets. Therefore, we have developed a coral-reef benchmark dataset and proposed a Multi-scale Attention Feature Fusion Network (MAFFN) as a neck part of the YOLOv5′s network, called “MAFFN_YOLOv5”. The MAFFN_YOLOv5 model outperforms the state-of-the-art object detectors, such as YOLOv5, YOLOX, and YOLOR, by improving the detection accuracy to 8.64%, 3.78%, and 18.05%, respectively, based on the mean average precision (mAP@.5), and 7.8%, 3.72%, and 17.87%, respectively, based on the mAP@.5:.95. Consequently, we have tested a hardware-based deep neural network for the detection of coral-reef health.

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Section: Related Workmentioning

confidence: 99%