The Accuracy of Remote Sensing Image Interprepation On Changes In Land Use Suitability In Merauke Regency Papua

Ismanto, Heru; Doloksaribu, Abner; Susanti, Diana Sri; Septarini, Dina Fitri

doi:10.14445/22315381/ijett-v68i10p207

Cited by 6 publications

(6 citation statements)

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“…Public cloud or external cloud is described from the perspective of mainstream awareness, which dynamically places resources on a fine-grained, self-service infrastructure on the Internet, and obtains third-party providers through network applications or network services to share resources and services [15][16]. Users access these resources and services through the Internet, but users do not own the resources of cloud computing.…”

Section: ) Public Cloudmentioning

confidence: 99%

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RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

2022

Soft Comput

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In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing, and then compares traditional methods from the selection of feature extraction methods and classifiers.The remote sensing image classification method is described, and finally the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the remote sensing image big data classification method based on cloud computing and convolutional neural network has more than a little improvement in accuracy than the traditional remote sensing image classification method, and the accuracy rate has reached 86.3%.

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Section: ) Public Cloudmentioning

confidence: 99%

“…Then, the sum of these matrices, plus the bias, is used as the input of the activation function, and finally the output characteristic map of the node is obtained. The output of the convolutional layer node can be expressed by formula (16):…”

Section: 𝑏 𝑗mentioning

confidence: 99%

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

2022

Soft Comput

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“…The classification suitability test attempts to determine the accuracy of classification results that have been carried out. This test aims to approximate the classification results to the actual data and determine the confidence level in using classification results for analysis and further purposes [21]. There are several ways to carry out a classification suitability test, particularly field validation and confusion matrix tests.…”

Section: ) Classification Suitability Testmentioning

confidence: 99%

Recognition of Agricultural Land-Use Change with Machine Learning-Based for Regional Food Security Assessment in Kulon Progo Plains Area

Nisa¹,

Pradipta²,

Sholikah³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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High conversion of agricultural land in Kulon Progo Regency, as such the construction of the Yogyakarta International Airport (YIA) and the Bedah Menoreh road, has resulted in food production and impacted food security, including Kulon Progo plains area. This study aimed to calculate the conversion rate of agricultural land and analyze its impact on food security in the Kulon Progo plains area from 2005 to 2020. The primary materials needed are Kulon Progo administrative maps, Landsat 7 and 8 images, land productivity data, population data, and consumption per capita data. With tools used is Google Earth Engine (GEE), SPSS 25, Google Earth Pro, and ArcGIS 10.3. The method used is calculating the Normalized Difference Vegetation Index (NDVI) and machine learningbased classification through GEE to identify land-use change and analyze the state of food security. The study proved that between 2015 and 2020, there was a conversion of paddy fields, with an average rate of 126 ha/year. The existence of new paddy fields influenced this land increase. However, in 2020 there is still food insecurity in Pengasih District, thus caused by the new paddy fields not being optimally used for rice growth. The productivity of the land produced is not optimal. With the availability of agricultural land in 2020 (1382.85 ha), food self-sufficiency will be limited for the next 24.75 years if there is no effort to increase paddy fields.

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“…The quantitative monitoring of remote sensing water quality has the advantages of large-scale, rapid and dynamic monitoring. The quantitative monitoring of water quality by technology has become a hot spot in water quality monitoring [1].…”

Section: Introductionmentioning

confidence: 99%

The Response of River Biological Monitoring and Water Quality Based on Remote Sensing Image Technology

2020

AJEB

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With the development of space technology, the temporal resolution, spatial resolution and spectral resolution of remote sensing images have been greatly improved in accuracy. In the quantitative remote sensing monitoring of water quality, these characteristics of remote sensing images will affect the monitoring The accuracy of the results will have a great impact. Therefore, in the process of quantitative remote sensing monitoring of water quality, the selection of remote sensing image data is very important. In this paper, remote sensing image technology is used to study the biological monitoring and water quality responsiveness of a certain river. After taking water samples, SPOT-5 is used to obtain remote sensing data. After correlation analysis, the correlation of water quality indicators is obtained, and the response analysis of water quality indicators and microorganisms is carried out. The experimental results show that the total amount of microorganisms has a good response relationship with water quality.

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The Accuracy of Remote Sensing Image Interprepation On Changes In Land Use Suitability In Merauke Regency Papua

Cited by 6 publications

References 0 publications

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

Recognition of Agricultural Land-Use Change with Machine Learning-Based for Regional Food Security Assessment in Kulon Progo Plains Area

The Response of River Biological Monitoring and Water Quality Based on Remote Sensing Image Technology

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