An efficient method to solve the classification problem for remote sensing image

Gao, Jianqiang; Xu, Lizhong

doi:10.1016/j.aeue.2014.09.001

Cited by 22 publications

(8 citation statements)

References 30 publications

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“…E 2 is closer to H 2 , therefore, the class of E 2 is the same of the class of H 5 . Also the class of E 3 is the same as the class of E 2 because only H 5 …”

Section: Ngementioning

confidence: 95%

“…In the k nearest neighbor (kNN) [2], and other kNN based methods [3][4][5], the decision are made by calculating the distance, which is a similarity measure [6], between the query and all the examples in the training set [7,8]. The optimum parameters such as weights and biases for an artificial neural network (ANN) are determined by utilizing whole training dataset, while training ANN by backpropagation [9].…”

Section: Introductionmentioning

confidence: 99%

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A joint generalized exemplar method for classification of massive datasets

Tağluk

Ertuğrul

2015

Applied Soft Computing

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“…E 2 is closer to H 2 , therefore, the class of E 2 is the same of the class of H 5 . Also the class of E 3 is the same as the class of E 2 because only H 5 …”

Section: Ngementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A joint generalized exemplar method for classification of massive datasets

Tağluk

Ertuğrul

2015

Applied Soft Computing

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“…Several methods can be employed to produce LULC by employing remote sensing data (Purkis & Klemas, 2011;Lakshmi et al, 2015;Al-doski et al, 2013). However, it should be noted that in case land surface objects have a similar reflectance or a small area, most of them could not provide high accurate maps (Gao & Xu, 2016). Using low radiometric resolution imageries, land classification can be a serious challenge because of spectral mixing of different surface elements and landscape complexity (Julien et al, 2011;Stenzel et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A hierarchical approach of hybrid image classification for land use and land cover mapping

Rahdari¹,

Soffianian²,

Pourmanafi³

et al. 2018

Geographica Pannonica

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Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC) in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.

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“…It characterizes the correlation among two samples of measurements taken from the classified area. With the use of the confusion matrix, measurement such as overall accuracy, user accuracy, producer accuracy and Kappa Coefficient can be calculated (Foody, 2002;Gao and Xu, 2014;Huang et al, 2002). The overall accuracy is determined by dividing the cumulative of the main diagonal items of the confusion matrix by the entire number of samples.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Manaf¹,

Mustapha²,

Sulaiman³

et al. 2016

Journal of Computer Science

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Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques. By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline. However, the result is meaningless if it has cloud and shadow on the water-land boundary. In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image. A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia. All the machine learning techniques have been tested on the three types of images. The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all. However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem. Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases.

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An efficient method to solve the classification problem for remote sensing image

Cited by 22 publications

References 30 publications

A joint generalized exemplar method for classification of massive datasets

A joint generalized exemplar method for classification of massive datasets

A hierarchical approach of hybrid image classification for land use and land cover mapping

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

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