Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting

Georganos, Stefanos; Grippa, Taïs; Vanhuysse, Sabine; Lennert, Moritz; Shimoni, Michal; Wolff, Éléonore

doi:10.1109/lgrs.2018.2803259

Cited by 216 publications

(115 citation statements)

References 25 publications

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“…We first implemented random forest and gradient boosting via XGBoost [71], two ensemble machine learning methods that have proven effective with the semantic segmentation of images across a number of disciplines including bioimage analysis [72][73][74][75][76][77][78][79] and remote sensing [80][81][82][83][84][85]. Unlike neural networks and deep learning methods, models trained with these algorithms benefit from simplicity and a relatively high level of interpretability [86][87][88].…”

Section: Random Forest and Gradient Boostingmentioning

confidence: 99%

Deep learning classification of lipid droplets in quantitative phase images

Sheneman

Stephanopoulos

Vasdekis

2020

Preprint

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We report the application of supervised machine learning to the automated classification of lipid-droplets in label-free, quantitative-phase images. By comparing various machine learning methods that are publicly available and commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, the implementation of all machine learning methods that we compared, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent replacement candidate of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism at the single-cell level.

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Section: Random Forest and Gradient Boostingmentioning

confidence: 99%

Deep learning classification of lipid droplets in quantitative phase images

Sheneman

Stephanopoulos

Vasdekis

2020

Preprint

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“…Recently, new ensemble learning algorithms, such as CCF (2015) and XgBoost (2016), have been explored and tested for the classification of remotely sensed images in a number of studies [26][27][28][29]. A relatively new ensemble learning algorithm, LightGBM (2017), has been introduced to the machine learning community, and has been received with great interest because it has outperformed the existing boosting frameworks in recent machine learning and data science competitions, especially in regards to complex datasets [43].…”

Section: Light Gradient Boosting Machinementioning

confidence: 99%

“…Recently, new ensemble learning algorithms, such as canonical correlation forest (CCF) (2015), extreme gradient boosting (XgBoost) (2016), and Light Gradient Boosting Machine (LightGBM) (2017), have been introduced to the machine learning community [23][24][25]. A very limited number of papers have been published regarding CCF [26,27] and XgBoost [28][29][30] for classification purposes in remote sensing; however, no study has been published yet using the recently launched LightGBM, which is a highly efficient gradient boosting decision tree that was developed by Microsoft Research in the field of remote sensing for classification purposes. Only in one paper by Liu, Ji, and Buchroithner [31] has LightGBM been tested, in this case for soil property retrieval by combining partial least squares.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Üstüner

Şanlı

2019

IJGI

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In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

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“…Stefanos Georganos et.al [2] implemented Extreme Gradient Boosting (XGBoost). For Very High Resolution (VHR) object based estimation of urban land cover.…”

Section: Related Workmentioning

confidence: 99%

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty¹,

Kumar²

2019

IJEAT

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Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

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Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting

Cited by 216 publications

References 25 publications

Deep learning classification of lipid droplets in quantitative phase images

Deep learning classification of lipid droplets in quantitative phase images

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Accurate Liver Disease Prediction with Extreme Gradient Boosting

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