Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

Beon, Mu‐Sup; Cho, Ki Hwan; Kim, Hyun Ok; Oh, Hye−Keun; Jeong, Jong-Chul

doi:10.3390/rs9030272

Cited by 5 publications

(2 citation statements)

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“…The process helps maintain low cost and hiring coverage of vegetation mapping. The proposed random forest classifier (RFC) achieved an accuracy of 92.4% [10]. A Bilateral extended short-term memory network is created for vegetation index mapping using European standard agricultural policy.…”

Section: Background Studymentioning

confidence: 99%

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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: Background Studymentioning

confidence: 99%

“…The comprehensive data with labels are not available for all dataset, and real-time dataset need to be analyzed. Hence random forest algorithm explained in [10,[25][26][27][28][29][30] needs to be improved. The proposed DMFM considers all these constraints and details the features, classification and computation time.…”

Section: Performance Metricsmentioning

confidence: 99%