Object-Oriented Random Forest Classification for Enteromorpha Prolifera Detection with SAR Images

2016 ◽  
Author(s):  
Cui Xie ◽  
Junyu Dong ◽  
Fangfang Sun ◽  
Lei Bing
Keyword(s):  
Random Forest ◽  
Object Oriented ◽  
Enteromorpha Prolifera ◽  
Sar Images ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals APPLING REMOTE SENSING TECHNIQUE TO MONITORING SPATIAL EXPANSION OF IMPORTANT CITIES IN CHINA-INDOCHINA PENINSULA ECONOMIC CORRIDOR

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1229-1235
Author(s):  
J. Li ◽  
H. Wang ◽  
G. Wang ◽  
H. Zhai ◽  
M. Han ◽  
...  
Keyword(s):  
Random Forest ◽  
Urban Expansion ◽  
Object Oriented ◽  
Impervious Surface ◽  
Indochina Peninsula ◽  
Landscape Pattern Metrics ◽  
Random Forest Classification ◽  
Urban Impervious Surface ◽  
Forest Classification ◽  
Expansion Speed

Since twentieth Century, the process of economic globalization has made great progress, and Southeast Asia has developed rapidly under the background of international industrial transferring. In this paper, the 6 important nodes cities in China - Indochina Peninsula along the economic corridor are took as study area. The main data is time series Landsat data. The method of object-oriented random forest classification was used to extract the classification results of study area from 2000 to 2015. The urban expansion of the node cities was evaluated by calculating the expansion speed of the impervious surface and the landscape pattern metrics. The results indicated that the method of object oriented random forest classification can effectively extract the urban impervious surface. the overall accuracy is over 81 %, and the Kappa coefficient is over 0.82. In the past 15 years, the expansion speed of Vientiane city was fastest in 6 countries. The area of urban impervious surface expanded 8 times than that of 2000.The pattern of expansion is summarized as “gather first-diffuse then”, “diffuse first-gather then” and “gather”. Overall, the process of urbanization of these cities are still in the rising period.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)

2019 ◽  
Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Water Body ◽  
Supervised Classification ◽  
Wheat Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Wheat Cultivation ◽  
Erdas Imagine ◽  
Sentinel 2

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.

scholarly journals Penerapan Metode Random Over-Under Sampling dan Random Forest Untuk Klasifikasi Penilaian Kredit

2018 ◽  
Vol 5 (2) ◽  
pp. 175-185
Author(s):  
Akhmad Syukron ◽  
Agus Subekti
Keyword(s):  
Random Forest ◽  
Financial Institution ◽  
Credit Scoring ◽  
Classification Performance ◽  
Random Forest Classification ◽  
Random Forest Method ◽  
Forest Classification ◽  
Improve Accuracy ◽  
Under Sampling ◽  
Accuracy Performance

                                         AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit,  meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit.  Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest  dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan  resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi  tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling                                                  AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling

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