A textural approach for land cover classification of remotely sensed image

Jenicka, S.; Suruliandi, A.

doi:10.1007/s40012-014-0038-4

Cited by 13 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The land uses by humans such as building clusters, farming lands, and green belts, and the crowns of natural vegetation tend to present recognizable patterns and regular configurations on satellite remotely sensed imagery [42]. Previous studies have proved that the GLCM texture metric (e.g., variation) is useful for improving the classification accuracy of various land cover types and reducing the classification errors for those objects with similar spectral features [22].…”

Section: Spectral Feature Selection and Optimizationmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

View full text Add to dashboard Cite

Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

show abstract

Section: Spectral Feature Selection and Optimizationmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

View full text Add to dashboard Cite

show abstract

“…This component vector represents color and texture properties in a local piece centered at the key in points [9] . While the number of key points and local methods varies from image to image, we cannot directly classifier with these data.…”

Section: Extracting the Local Methods Of The Imagesmentioning

confidence: 99%

Routine identification of melanoma disease using global and local features

Kavya¹,

Saranya²

2016

2016 International Conference on Computer Communication and Informatics (ICCCI)

View full text Add to dashboard Cite

This paper focuses on the finding, segmentation, categorization and removal of skin lesion as a literature survey. Melanoma is a category of cancer that develop from the pigment-network cells renowned as melanocytes. Melanomas usually develop in the skin other than may arise in the maw, backbone or ogle. This paper addresses two different systems for finding of fur evil in dermoscopy images. The first system uses global features and the second system uses local methods and the classifier. Therefore, melanoma is simply to identify with help of global features and local methods.

show abstract

“…For additional information from the radar images, grey level co-occurrence matrices (GLCM) were used to compute texture measures. According to [61,62], such measures capture the spatial relationships of pixels by identifying the pattern based on the neighborhood size provided. The resultant matrices store the occurrence frequency of pixel pairs with specific grey level (G) or pixel brightness values [61].…”

Section: Image Preprocessingmentioning

confidence: 99%

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021

View full text Add to dashboard Cite

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

show abstract

A textural approach for land cover classification of remotely sensed image

Cited by 13 publications

References 14 publications

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Routine identification of melanoma disease using global and local features

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

Contact Info

Product

Resources

About