Satellite image retrieval of random forest (rf-PNN) based probablistic neural network

Devi, N. Bharatha

doi:10.1007/s12145-021-00759-3

Cited by 12 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the classification standard of social organizations in China, social organizations are divided into three categories: social groups, foundations and social service institutions. At present, only a few domestic research institutions regulate social organizations and non-profit organizations through laws, regulations or administrative rules [15,16].…”

Section: Research On the Status Quo Of Natural Protection Environmentmentioning

confidence: 99%

Nature Conservation Environment Based on Public Participation of Random Forests

2023

NEP

View full text Add to dashboard Cite

With the development of social economy, the public's awareness of participating in the natural protection of the environment has gradually increased, and a certain organizational structure and interest expression mechanism have gradually formed in the natural protection of the environment. The public's participation in natural environment protection is also increasingly concerned by government departments, social organizations and enterprises. Based on the random forest model, combined with public participation activities and management practice experience, this paper explores the problems and solutions of public participation in natural protection environment. The results show that: in the questionnaire survey of public environmental awareness, 164 people said "very important", 92.1%; Thirteen respondents thought it was "important", 7.3% of all respondents. There are 177 items in total, about 99.4% of all respondents. This shows that most people are still aware of environmental protection, but lack guidance.

show abstract

Section: Research On the Status Quo Of Natural Protection Environmentmentioning

confidence: 99%

Nature Conservation Environment Based on Public Participation of Random Forests

2023

NEP

View full text Add to dashboard Cite

show abstract

“…In this analysis process, this paper measures the similarity between two street art portraits based on Euclidean distance. 21 x and y correspond to the feature vectors of both, and the corresponding Euclidean distance expressions are as follows:…”

Section: Similarity Metricmentioning

confidence: 99%

(Retracted) Design of street art image retrieval system based on virtual simulation technology for the public health environment

Li¹,

Hua²

2023

J. Electron. Imag.

View full text Add to dashboard Cite

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.ZL and JH either did not respond directly or could not be reached.

show abstract

“…In machine learning, a thorough dataset is created that includes all of the system parameters. ML is beneficial in situations where theoretical knowledge alone is insufficient to anticipate some facts (5,6) . It has a wide range of applications, including land use and cover problems (7) , disaster management, and climate change (8) .…”

Section: Introductionmentioning

confidence: 99%

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Vinuja,

Devi,

Mary

2024

IJST

View full text Add to dashboard Cite

Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved ChanVese Segmentation, Hybrid HRSVMCNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray level cooccurrence matrix

show abstract

Satellite image retrieval of random forest (rf-PNN) based probablistic neural network

Cited by 12 publications

References 12 publications

Nature Conservation Environment Based on Public Participation of Random Forests

Nature Conservation Environment Based on Public Participation of Random Forests

(Retracted) Design of street art image retrieval system based on virtual simulation technology for the public health environment

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Contact Info

Product

Resources

About