Classification of Remotely Sensed Algal Blooms along the Coast of India using Support Vector Machines and Regularized Least Squares

Babu, M. Jocelyn; Geetha, P.

doi:10.17485/ijst/2016/v9i30/99001

Cited by 3 publications

References 10 publications

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Urban Sprawl Assessment Using Remote Sensing and GIS Techniques: A Case Study of Ernakulam District

Radhakrishnan

Geetha

2021

Intelligent Sustainable Systems

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Urban sprawl is seen as a huge global challenge that primarily affects emerging countries with large populations. The outcomes of unplanned city growth into adjacent rural areas are uncontrolled wiping of natural resources and creating fluctuations in land surface temperature. This, in turn, influences climate change by causing heatwaves, droughts, and other extreme weather events. The present study uses spatial analytic techniques of RS and GIS to assess the urban sprawl in Ernakulam District of Kerala, India. Determining the Land Use/Land Cover (LULC), Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) help to better understand how built-up areas and vegetation impact the surface temperature. Observations using Landsat 8 images from 2014 and 2020 showed that built-up areas have expanded by 2%, while waterbodies/wetlands have decreased by 1%. During the same period maximum, LST climbed from 34.3 °C in 2014 to 43.16 °C in 2020. Without proper urban planning, increasing surface temperature coupled with unchecked urban growth will harm the environment and quality of life. Hence an effective urban growth plan is required to limit urban sprawl.

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Urban Sprawl Assessment Using Remote Sensing and GIS Techniques: A Case Study of Ernakulam District

Radhakrishnan

Geetha

2021

Intelligent Sustainable Systems

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Hybrid generative model for grading the severity of diabetic retinopathy images

Bhuvaneswari,

Diviya,

Subramanian

et al. 2023

Computer Methods in Biomechanics and Biomedical Engineering: Im

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An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment

et al. 2022

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Algae are widely distributed and have a considerable impact on water quality. Harmful algae can degrade water quality and be detrimental to aquaculture, while beneficial algae are widely used. The accuracy and speed of existing intelligent algae detection methods are available, but the size of parameters of models is large, the equipment requirements are high, the deployment costs are high, and there is still little research on lightweight detection methods in the area of algae detection. In this paper, we propose an improved Algae-YOLO object detection approach, which is based on ShuffleNetV2 as the YOLO backbone network to reduce the parameter space, adapting the ECA attention mechanism to improve detection accuracy, and redesigning the neck structure replacing the neck structure with ghost convolution module for reducing the size of parameters, finally the method achieved the comparable accuracy. Experiments showed that the Algal-YOLO approach in this paper reduces the size of parameters by 82.3%, and the computation (FLOPs) is decreased from 16G to 2.9G with less loss of accuracy, and mAP by only 0.007 when compared to the original YOLOv5s. With high accuracy, the smaller model size are achieved, which reduces the equipment cost during actual deployment and helps to promote the practical application of algae detection.

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Classification of Remotely Sensed Algal Blooms along the Coast of India using Support Vector Machines and Regularized Least Squares

Cited by 3 publications

References 10 publications

Urban Sprawl Assessment Using Remote Sensing and GIS Techniques: A Case Study of Ernakulam District

Urban Sprawl Assessment Using Remote Sensing and GIS Techniques: A Case Study of Ernakulam District

Hybrid generative model for grading the severity of diabetic retinopathy images

An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment

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