Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

Isaac, Ebenezer R. H. P.; Easwarakumar, K. S.; Isaac, Joseph

doi:10.1080/2150704x.2016.1274443

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…For the same classification problem, different classifiers have different classification results. Some researchers have reported that even though no remarkable difference exists amongst different classifiers at the overall level, such dissimilarities can be found at the per-class level [16]- [19], [21]- [22]. Such situation is suitable for the base classifiers of DLS.…”

Section: A Diversity Of Base Classifiers In Dslmentioning

confidence: 93%

Remote Sensing Image Classification Using Deep–Shallow Learning

Dou

Shen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, classification using multiple classifier system (MCS) has been reported as an effective method to improve remote sensing (RS) image classification. Such systems provide a complementary mechanism to use multiple classifiers, which have shallow architecture to solve the same classification problem; however, the system exhibits shortcomings due to complex ensemble strategy. Deep learning (DL) has been proven to be an advanced method for complex data classification; however, how to use its advantages to overcome the shortcomings of MCS in ensemble strategy for classification accuracy improvement is worthy of study. Thus, with the multiple classifier mechanism and DL architecture, we propose a novel RS image classification framework, namely, deep-shallow learning (DSL), to improve classification accuracy. The DSL framework consists of a shallow learning (SL) layer and a DL layer. The SL layer contains various classifiers with shallow architecture, which can output different classification results for a certain input, whereas the DL layer is formed by DL networks, which can continue learning from the outputs of the SL layer. DSL simulates a human thinking model that continuously learns from the existing learnings to improve learning efficiency. In our experiment, three shallow classification algorithms, i.e. C4.5, k-nearest neighbour, and naive Bayesian, are used to train base classifiers in the SL layer, whereas a deep belief network (DBN) is used to train the DL layer. The experiment results on three different datasets indicate that DSL outperforms other methods in terms of classification accuracy by using backpropagation neural network, bagging, AdaBoost, random forest, multilayer perceptron and DBN.

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Section: A Diversity Of Base Classifiers In Dslmentioning

confidence: 93%

Remote Sensing Image Classification Using Deep–Shallow Learning

Dou

Shen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Overall, the random forest model's classification power is strongly influenced by the number of subtrees used, as a result of its diverse characteristics; therefore, this parameter should be carefully considered. As suggested in previous works [63][64][65], a total of 500 subtrees were used in this project.…”

Section: Landsat Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

Bình¹,

Nhut²,

An³

et al. 2021

IJGI

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The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

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“…e key to the algorithm is to determine the number of variables and the number of decision trees. e algorithm does not overfit as the number of trees increases, has good generalization performance, is more robust, and is suitable for dealing with high-dimensional, nonlinear complex problems [25,26].…”

Section: Random Forest (Rf) Algorithm Random Forest Wasmentioning

confidence: 99%

“…However, it is not the case that the larger the two parameters are, the higher the accuracy is. After several experiments, the model accuracy regression is higher and stable when k ∈ [220, 290] and m ∈ [18,26] according to the robustness of the results. e final test found that the effect was optimal when k � 260 and m � 22. e comparison of the effectiveness of the above six machine learning algorithm methods is shown in Figure 6.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

Liu

Zhang

Jiang

et al. 2021

Journal of Spectroscopy

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Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.

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Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

Cited by 23 publications

References 12 publications

Remote Sensing Image Classification Using Deep–Shallow Learning

Remote Sensing Image Classification Using Deep–Shallow Learning

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

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