Combining the decision tree and supervised classification techniques to identify tobacco fields in satellite images: Luxi County of Yunnan Province in China as an example

2006 ◽  
Author(s):  
Xuexia Zhang ◽  
Weihong Cui ◽  
Zhenguo Niu ◽  
Jinggang Li ◽  
Na Zhao
Keyword(s):  
Decision Tree ◽  
Supervised Classification ◽  
Yunnan Province ◽  
Satellite Images ◽  
Classification Techniques

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

A comparative study to recognize fake ratings in recommendation system using classification techniques

2021 ◽  
pp. 1-8
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Decision Tree ◽  
False Positive ◽  
Recommendation System ◽  
Target Item ◽  
User Profile ◽  
Method Performance ◽  
Classification Techniques ◽  
Attack Model ◽  
Unique Method ◽  
Decision Tree Method

The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed using push attack count to reduce the false positive rates from the doubtful user profile. The proposed model is evaluated with detection rate and false positive rates by considering the filler size and attacks size. The experiment was conducted with 6%, 8% and 10% filler sizes and with different attack sizes that ranges from 0%–100%. Various classification techniques such as decision tree, logistic regression, SVM and random forest methods are used to classify the fake ratings. From the results, it is witnessed that SVM model works better with random and bandwagon attack models at an average of 4% higher accuracy. Similarly the decision tree method performance better at an average of 3% on average attack model.

scholarly journals Pemetaan Klasifikasi Dan Analisa Perubahan Ekosistem Mangrove Menggunakan Citra Satelit Multi Temporal Di Karimunjawa, Jepara, Indonesia

2018 ◽  
Vol 21 (2) ◽  
pp. 97
Author(s):  
Nurul Latifah ◽  
Sigit Febrianto ◽  
Hadi Endrawati ◽  
Muhammad Zainuri
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Mangrove Ecosystem ◽  
Nursery Ground ◽  
Fish Ponds ◽  
Natural Phenomena ◽  
Key Factors ◽  
Marine Life ◽  
Fisheries Resources ◽  
Multi Temporal

Mapping of Classification and Analysis of Changes in Mangrove Ecosystem Using Multi-Temporal Satellite Images in Karimunjawa, Jepara, Indonesia  Mangrove ecosystem is one of the three ecosystem in the coastal area which has important ecological role in supporting marine life and fisheries resources. These important roles include spawning ground and nursery ground for various marine organisms. However, in the last decades, mangrove ecosystem has been undergoing significant degradation. The aim of this research is to quantify the changes of mangrove coverage and density in Karimunjawa as well as key-factors leading to the changes. Supervised classification method (83% accuracy and Kappa coefficient 0.73%) was applied to satellite images to identify the temporal changes in mangrove coverage. Mangrove density was quantified using NDVI algorithm and NIR-RED wavelength. The result shows that during three periods of observed data, changes in mangrove coverage were significant: 126.81 ha increase (1992 – 2003); 82.37 ha decrease (1992 – 2017); and 209.18 ha decrease (2003 – 2017). Mangrove density in most part of Karimunjawa belongs to the category of ‘low’ (NDVI value: -1 – 0.33). Key factors contributing to the decrease mangrove coverage are deforestation, natural phenomena, land conversion into fish ponds and hotels. The only increase in the year 1992 – 2003 was caused by high sedimentation level that allows more mangroves to grow. Overall, the methods in this research could be used as an approach to describe to effectively monitor the changes of mangrove coverage in an area as basic data for sustainable environmental management. Ekosistem mangrove merupakan salah satu dari tiga ekosistem pesisir yang memiliki peranan ekologis penting dalam mendukung kehidupan dan keberlangsungan dari sumberdaya perikanan.  Hal tersebut dikarenakan fungsi mangrove sebagai tempat memijah dan asuhan bagi banyak biota air. Beberapa dekade terakhir keberadaan dari ekosisitem mangrove mengalami degradasi yang sangat signifikan. Tujuan dari penelitian ini adalah untuk mengetahui perubahan luasan dan kerapatan mangrove dan mengidentifikasi faktor penyebabnya.  Metode analisa perubahan luasan mangrove menggunakan citra satelit multi temporal dengan dilakukan pembuatan klasifikasi menggunakan metode supervised classification dengan nilai akurasi total 83% dengan Kappa koefisien 0,73%.  Setelah terseleksi antara mangrove dan non mangrove maka dilakukan perhitungan kerapatan tajuk menggunakan algoritma NDVI dengan memanfaatkan panjang gelombang NIR dan RED.  Hasil analisa spasial dengan rentang 3 tahun berbeda didapatkan perubahan penurunan dan penambahan luasan mangrove terjadi secara signifikan: tahun 1992 – 2003 telah terjadi penambahan luasan sebesar 126,81 ha; tahun 1992–2017 terjadi perubahan luasan sebesar 82,37 ha;  tahun 2003–2017 terjadi perubahan luasan sebesar 209,18 ha.  Kerapatan mangrove di Karimunjawa sebagian besar tergolong kategori kerapatan jarang dengan nilai NDVI antara -1 – 0,33. Faktor utama penyebab penurunan luasan mangrove antara lain penebangan liar, faktor alam, perubahan fungsi lahan menjadi pertambakan dan perhotelan.  Penambahan luasan mangrove terjadi pada antara tahun1992 sampai 2003 hal tersebut disebabkan sedimentasi yang menumpuk di pantai dan sudah ditumbuhi oleh mangrove.  Secara keseluruhan metode ini dapat menggambarkan perubahan secara efektif serta hasilnya dapat dipergunakan untuk pemantauan dan perencanaan ekosistem mangrove di suatu wilayah. 

Applying Supervised Clustering to Landsat MSS Images into GIS-Application

2013 ◽  
Vol 4 (3) ◽  
pp. 114-122
Author(s):  
Miguel Torres ◽  
Marco Moreno-Ibarra ◽  
Rolando Quintero ◽  
Giovanni Guzmán
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Training Data ◽  
Clustering Method ◽  
Maximum Likelihood Classification ◽  
Gis Application ◽  
Supervised Clustering ◽  
Gis Applications ◽  
Geographic Environment ◽  
Decision Making Model

In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data related to the geographic environment. The algorithm has been integrated into Spatial Analyzer Module to improve the decision making model and the spatial analysis into GIS-applications.

Crop Decision Tree Classification Extraction Based on MODIS NDVI in Beijing

2014 ◽  
Vol 955-959 ◽  
pp. 787-790 ◽  
Author(s):  
Shi Wei Dong ◽  
Dan Feng Sun ◽  
Hong Li
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Seasonal Variation ◽  
Decision Tree ◽  
Land Surface ◽  
Satellite Images ◽  
Overwhelming Majority ◽  
Modis Ndvi ◽  
Decision Tree Classification ◽  
Double Cropping

Time-series satellite images can reflect the seasonal variation from vegetation on land surface. Single cropping and double cropping were extracted by decision tree classification based on MODIS NDVI of Beijing in 2007, and spatial distribution of dominant crops in Beijing was obtained. The dominant crops of single cropping were maize, wheat and vegetable, and the overwhelming majority of crops with double cropping were wheat-maize. The results showed that this method could effectively determine the dominated crops in Beijing.

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