Land Cover/Use Classification Based on Feature Selection

Zhang, Yuwei; Liu, Jinting; Wan, Luhe; Qi, Shaoqun

doi:10.2112/si73-067.1

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…SVM is a new machine-learning method developed based on statistical learning theory and the principle of structural risk minimization [39]. Compared with the traditional learning methods, it has the characteristics of high accuracy, fast calculation speed, and strong generalization ability, which is widely used in image and land classification mapping [19,40]. The basic idea of SVM classification is to transform the input space into a high-dimensional feature space through nonlinear transformation, and then further find the optimal hyperplane (OHP) in this new high-dimensional feature space.…”

Section: Support Vector Machinementioning

confidence: 99%

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Mao

Hou

et al. 2020

Remote Sensing

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Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.

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Section: Support Vector Machinementioning

confidence: 99%

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Mao

Hou

et al. 2020

Remote Sensing

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“…Meanwhile, the land cover quality index is measured based on the land cover area. The LCQI was calculated by comparing the forest and administrative area (DIC, 2019;Wear et al, 1998;Zhang et al, 2015 Based on the Law of the Republic of Indonesia number 41 of 1999, each province has a minimum forest area of about thirty per cent of the total area (IGRL RI, 1999).…”

Section: Methodsmentioning

confidence: 99%

Application of Block-Based K-Medoids and Ward's Method to Classify Provinces in Indonesia Based on Environmental Quality Index

Kariyam

2023

Eduvest

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Environmental quality is an essential aspect of the world's sustainability. This paper presents a combination of several methods to obtain a profile of provinces in Indonesia according to the environmental quality indices. We apply the block-based k-medoids algorithm, Ward's method, and the Hartigan index to determine the number of clusters. Based on Hartigan index suggested classifying 34 provinces in Indonesia into four groups. A comparison of the mean vector of four groups using multivariate analysis of variance concluded that three areas have the best environment quality index and eight regions require serious attention to improve their environmental quality methods.

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“…By focusing on spatially related objects, object-based methods can retrieve more information and mitigate the adverse effects of noise compared to pixel-based methods. Alternatively, classification accuracy and data fusion can be improved using decision trees, which allow to integrate different information, such as polarimetric scattering information and optical information, at the decision level (Chen et al , 2014; Zhang et al , 2015; Yang et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

Chen

et al. 2021

DTA

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PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

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Land Cover/Use Classification Based on Feature Selection

Abstract: Zhang, Y.; Liu, J.; Wan, L., and Qi, S., 2015. Land cover/use classification based on feature selection.

Cited by 4 publications

References 6 publications

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Application of Block-Based K-Medoids and Ward's Method to Classify Provinces in Indonesia Based on Environmental Quality Index

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

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