Evaluation of deep learning and transform domain feature extraction techniques for land cover classification: balancing through augmentation

Parikh, Hemani; Patel, Samir; Patel, Vibha

doi:10.1007/s11356-022-23105-6

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…To experiment with the features generated by the model feature fusion, they used a dataset for their experiments. The experimental results proved its effectiveness (Parikh et al, 2023). Klonecki et al (2023) argued that most vehicle driving methods do not take into account the cost information associated with features, for which they design a cost-constrained feature selection problem.…”

Section: Introductionmentioning

confidence: 92%

“…The experimental results indicated that the location estimation accuracy followed the resolution of the implementation of the method (Ludwig-Barbosa et al, 2023). Parikh et al (2023) argued that temporal features in vehicle signals are affected by a variety of complex factors, and that identifying features that improve classification accuracy is a major problem in vehicle operation research. They compared transform domain feature extraction for different state classifications and evaluated its feature generation capability using a convolutional autoencoder.…”

Section: Introductionmentioning

confidence: 97%

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Improvement of action recognition based on ANN-BP algorithm for auto driving cars

Tian,

Tan

2024

Front. Mech. Eng.

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Introduction: With the development of artificial intelligence and autonomous driving technology, the application of motion recognition in automotive autonomous driving is becoming more and more important. The traditional feature extraction method uses adaptive search hybrid learning and needs to design the feature extraction process manually, which is difficult to meet the recognition requirements in complex environments.Methods: In this paper, a fusion algorithm is proposed to classify the driving characteristics through time-frequency analysis, and perform backpropagation operation in artificial neural network to improve the convergence speed of the algorithm. The performance analysis experiments of the study were carried out on Autov data sets, and the results were compared with those of the other three algorithms.Results: When the vehicle action coefficient is 227, the judgment accuracy of the four algorithms is 0.98, 0.94, 0.93 and 0.95, respectively, indicating that the fusion algorithm is stable. When the road sample is 547, the vehicle driving ability of the fusion algorithm is 4.7, which is the best performance among the four algorithms, indicating that the fusion algorithm has strong adaptability.Discussion: The results show that the fusion algorithm has practical significance in improving the autonomous operation ability of autonomous vehicles, reducing the frequency of vehicle accidents during driving, and contributing to the development of production, life and society.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 97%

Improvement of action recognition based on ANN-BP algorithm for auto driving cars

Tian,

Tan

2024

Front. Mech. Eng.

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A systematic review of open data in agriculture

Chamorro-Padial,

García,

Gil

2024

Computers and Electronics in Agriculture

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Distribution and Long-Term Variation of Wetland Land Cover Types in the Yellow River Delta Remote Sensing Monitoring

Zhou,

Zhao,

et al. 2024

JMSE

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Wetlands are dubbed the “kidneys of the earth” and are involved in climate regulation, carbon sequestration, ecological balance preservation, and reducing the surface water pollution. Ongoing economic development has introduced pressing challenges to wetland environments. In this context, extracting coastal wetland information and monitoring the dynamic changes are essential. Using long-term sequence Sentinel-2 satellite remote sensing images and field observations, this research proposed a Dynamic Bayesian Network classification model framework based on conjugate gradient updates. We compared the wetland feature extraction effects of the Fletcher–Reeves and the Polak–Ribière–Polyak algorithms of the conjugate gradient. Then, remote sensing combined with the FRDBN classification model was used to extract the information pertinent to wetland feature types and changes in wetland areas and analyze alterations in the distribution characteristics of land cover types. The results showed that the FRDBN model achieved high accuracy (above 96%), and kappa coefficients exceeded 0.96. Long-term monitoring revealed that the area of wetlands increased by 0.85 × 104 hm2 from 2016 to 2021. Non-aquatic land cover types exhibited pronounced dynamic changes, with the area of change representing 58–69% of the monitored total. Specifically, the transition between salt marsh vegetation and artificial wetlands was relatively obvious. The FRDBN model provides a new method for extracting wetland feature information. Wetland protection, dynamic monitoring, and carbon sink research can provide robust technology support, facilitating investigations into coastal salt marsh carbon sinks and technological advances in carbon sink assessment.

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Evaluation of deep learning and transform domain feature extraction techniques for land cover classification: balancing through augmentation

Cited by 3 publications

References 47 publications

Improvement of action recognition based on ANN-BP algorithm for auto driving cars

Improvement of action recognition based on ANN-BP algorithm for auto driving cars

A systematic review of open data in agriculture

Distribution and Long-Term Variation of Wetland Land Cover Types in the Yellow River Delta Remote Sensing Monitoring

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