Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Hao, Fei; Fan, Zehua; Wang, Chengkun; Zhang, Nannan; Wang, Tao; Chen, Rengu; Bai, Tiecheng

doi:10.3390/rs14040829

Cited by 61 publications

(36 citation statements)

References 60 publications

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“…The application of RFE in combination with the property of RF variable importance in this study helped in determining the input features and their contribution in producing the optimal classification accuracy in the study area. This subset of the relevant features is a common appropriate approach for building robust learning models [31].…”

Section: Discussionmentioning

confidence: 99%

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices

Saeed¹,

Othman²

2023

IJACSA

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Adding spectral indices to Sentinel-2 spectral bands to improve land-cover (LC) classification with limited sample size can affect the accuracy due to the curse of dimensionality. In this study, we compared the performance metrics of Random Forest (RF) classifier with three different combinations of features for land cover classification in an urban arid area. The first combination used the ten Sentinel-2 bands with 10 and 20 m spatial resolution. The second combination consisted of the first combination in addition to five common spectral indices (15 features). The third combination represented the best output of features in terms of performance metrics after applying recursive feature elimination (RFE) for the second combination. The results showed that applying RFE reduced the number of features in combination 2 from 15 to 8 and the average F 1 -score indicator increased by nearly 8 and 6 percent in comparison with using the other two combinations respectively. The findings of this study confirmed the importance of feature selection in improving LC classification accuracy in arid areas through removing the redundant variable when using limited sample size and using spectral indices with spectral bands, respectively.

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Section: Discussionmentioning

confidence: 99%

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices

Saeed¹,

Othman²

2023

IJACSA

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“…Nevertheless, because of the shortcoming that it is very sensitive to its initialization process and training process and easy to overfit (Rothmann and Porrmann, 2022;Matsuo et al, 2022), reinforcement learning should be improved further around the issue of this article. In this respect, the random forest algorithm could be introduced to solve the overfitting problem (Ouadah et al, 2022;Fei et al, 2022). Taking the two algorithms together, this paper proposes a two-step random forest reinforcement learning algorithm.…”

Section: Methods Proposition 41 Algorithm Outlinementioning

confidence: 99%

“…In this respect, the random forest algorithm could be introduced to solve the overfitting problem (Ouadah et al. , 2022; Fei et al. , 2022).…”

Section: Methods Propositionmentioning

confidence: 99%

Sharing network features analysis and dispatching strategy design

Shi

2022

IMDS

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PurposeA vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.Design/methodology/approachTo that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.FindingsThe authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.Originality/valueBecause the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

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“…Since the relationships between different vegetation patches in urban ecosystems are highly complex and nonlinear, random forest (RF) and k-nearest neighbor (KNN), both of which can be used for multi-class and nonlinear classification, were the preferred classifiers tested in this study. RF, which combines Bagging integrated learning theory with a random subspace method, is an integrated algorithm, based on decision trees, with high accuracy in the application of massive data-based fast image classification [51][52][53]. KNN is also an effective supervised machine learning method, a lazy learning algorithm that allows the class of each target object to be determined by the k nearest training sample objects in the feature space, according to the Euclidean distance (ED) minimum or majority voting criterion [54].…”

Section: Urban Green Space Classificationmentioning

confidence: 99%

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

Xiao

Chen

Zhang

et al. 2022

Forests

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High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

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Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Cited by 61 publications

References 60 publications

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices

Sharing network features analysis and dispatching strategy design

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

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