scholarly journals Developments in Landsat Land Cover Classification Methods: A Review

2017 ◽  
Vol 9 (9) ◽  
pp. 967 ◽  
Author(s):  
◽  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Classification Methods

scholarly journals Refining Land-Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very-High-Resolution Remote Sensing Images

2018 ◽  
Author(s):  
Cui Guo Qing ◽  
Lv Zhi Yong ◽  
Li GuangFei ◽  
Jón Atli Benediktsson ◽  
Lu Yu Dong
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Classification ◽  
Majority Voting ◽  
Classification Methods ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Central Pixel ◽  
Voting Strategy ◽  
Very High

Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

scholarly journals Land Use and Land Cover Classification from ETM Sensor Data : A Case Study from Tamakoshi River Basin of Nepal

2014 ◽  
Vol XL-8 ◽  
pp. 943-948
Author(s):  
U. S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Classification Methods ◽  
Barren Land ◽  
Grass Land ◽  
Mountain Watershed

The mountain watershed of Nepal is highly rugged, inaccessible and difficult for acquiring field data. The application of ETM sensor Data Sat satellite image of 30 meter pixel resolutions has been used for land use and land cover classification of Tamakoshi River Basin (TRB) of Nepal. The paper tries to examine the strength of image classification methods in derivation of land use and land classification. Supervised digital image classification techniques was used for examination the thematic classification. Field verification, Google earth image, aerial photographs, topographical sheet and GPS locations were used for land use and land cover type classification, selecting training samples and assessing accuracy of classification results. Six major land use and land cover types: forest land, water bodies, bush/grass land, barren land, snow land and agricultural land was extracted using the method. Moreover, there is spatial variation of statistics of classified land uses and land cover types depending upon the classification methods. <br><br> The image data revealed that the major portion of the surface area is covered by unclassified bush and grass land covering 34.62 per cent followed by barren land (28 per cent). The knowledge derived from supervised classification was applied for the study. The result based on the field survey of the area during July 2014 also verifies the same result. So image classification is found more reliable in land use and land cover classification of mountain watershed of Nepal.

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