Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms

He, Tao; Sun, Yong; Xu, Jing; Wang, Xuejun; Hu, Chang-Ru

doi:10.1117/1.jrs.8.083636

Cited by 25 publications

(10 citation statements)

References 50 publications

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“…Boser et al. [ 47 ] proposed a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplane [ 42 , 48 ]. He et al.…”

Section: Methodsmentioning

confidence: 99%

“…An optimal separating hyperplane refers to the decision boundary that lessens misclassifications attained during the training step [46]. Boser et al [47] proposed a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplane [42,48]. He et al [42] gave detailed information regarding the SVM algorithm.…”

Section: Texture Feature Image Classificationmentioning

confidence: 99%

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Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Chen

Useya

Mugiyo

2020

Heliyon

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Radar imagery have few polarization bands which can limit the ability to do traditional digital classification. Harmonization of Sentinel-1 and Landsat 8 data despite having complementary texture information can be a challenge. The objectives of this paper are to explore texture features derived from Landsat 8 OLI and dualpolarized Sentinel-1 SAR speckle filtered and unfiltered backscatter, to aggregate classification results using Decision-Level Fusion (DLF), and to evaluate the performance of decision-level fused maps. Gray Level Cooccurrence Matrix (GLCM) is employed to derive sets of seven texture features for Landsat 8 bands and VV þ VH backscatter using 5 Â 5, 7 Â 7, 9 Â 9, and 11 Â 11 window sizes. Each texture feature is stacked with a respective source image and classified using Support Vector Machine (SVM). Classified maps from the best three performers from both speckle filtered and unfiltered are aggregated with classified maps from Landsat 8 using plurality voting algorithm and compared using Z-test. Results indicate an overall classification accuracy of 96.02% from DLF images of Landsat and non-speckle filtered maps, whereas Landsat and speckle filtered achieved 94.69%. The best texture information are derived from the blue band followed by the red band, whereas speckle unfiltered textures performed better than speckle filtered textures. We conclude that integration of Landsat 8 and Sentinel-1, either speckle filtered or unfiltered, improves crop classification and speckles do not have statistically significant effects (p ¼ 0.1208).

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“…Boser et al. [ 47 ] proposed a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplane [ 42 , 48 ]. He et al.…”

Section: Methodsmentioning

confidence: 99%

Section: Texture Feature Image Classificationmentioning

confidence: 99%

Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Chen

Useya

Mugiyo

2020

Heliyon

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“…According to whether there is a priori knowledge of the spectrum, the target detection algorithm can be divided into two categories: supervised and unsupervised. This study mainly used -means [47] unsupervised classification, Mahalanobis distance [48], minimum distance [49], and SVM [50] for supervision classification. Mahalanobis distance, minimum distance, and SVM were applied to explore all the input features according to given training samples.…”

Section: Other Classification Methodsmentioning

confidence: 99%

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Zhao

Zhang

Pan

2017

Mathematical Problems in Engineering

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A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of -means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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“…The development of machine learning has allowed researchers to use machine learning abilities to improve pixel feature extraction. However, early machine learning methods such as neural networks [26,27], support vector machines [28,29], decision trees [30,31], and random forests [32,33] still use pixel spectral information as input. Although these methods can be effective at obtaining features, these remain single-pixel features, without utilizing the spatial relationships between adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Zhang

et al. 2020

Remote Sensing

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Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

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Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms

Cited by 25 publications

References 50 publications

Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

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