Classification of Chlorophyll Concentrations in Coastal Water Using Linear Regression with THEOS Imagery

Kzar, Ahmed Asal; Abdulbaqi, Hayder Saad; Z, MatJafri M.; S, Lim H.

doi:10.14445/23500301/ijap-v7i1p120

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…It provides better results when applied to the raw data. The goal of the feature extraction process with the machine learning algorithm is discussed here, like Random Forest (RF), Decision Tree (DT) [22] and logistic regression (LR) to provide a multi-variation data pattern that provides unique identification of the input pattern [23]. The original image data from the dataset is transformed into pixels processing, and the originality of the image pixels is also secured.…”

Section: Significance Of Machine Learning Models In Vegetation Featur...mentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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Section: Significance Of Machine Learning Models In Vegetation Featur...mentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

Classification of Chlorophyll Concentrations in Coastal Water Using Linear Regression with THEOS Imagery

Cited by 1 publication

References 14 publications

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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