A fuzzy classification of sub-urban land cover from remotely sensed imagery

1998 ◽  
Vol 19 (14) ◽  
pp. 2721-2738 ◽  
Author(s):  
J. Zhang ◽  
G. M. Foody
Keyword(s):  
Land Cover ◽  
Remotely Sensed ◽  
Urban Land ◽  
Fuzzy Classification ◽  
Remotely Sensed Imagery ◽  
Urban Land Cover

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover

2016 ◽  
Vol 44 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Masoud Habibi ◽  
Mahmod Reza Sahebi ◽  
Yasser Maghsoudi ◽  
Shaheen Ghayourmanesh
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Multiple Classifiers ◽  
Urban Land Cover ◽  
Object Based ◽  
Sar Data

scholarly journals A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery

2020 ◽  
Vol 12 (5) ◽  
pp. 862
Author(s):  
Sicong Liu ◽  
Qing Hu ◽  
Xiaohua Tong ◽  
Junshi Xia ◽  
Qian Du ◽  
...  
Keyword(s):  
Feature Selection ◽  
High Resolution ◽  
Land Cover ◽  
Computational Efficiency ◽  
Remotely Sensed ◽  
Guided Filter ◽  
Multi Scale ◽  
Remotely Sensed Imagery ◽  
Very High

In this article, a novel feature selection-based multi-scale superpixel-based guided filter (FS-MSGF) method for classification of very-high-resolution (VHR) remotely sensed imagery is proposed. Improved from the original guided filter (GF) algorithm used in the classification, the guidance image in the proposed approach is constructed based on the superpixel-level segmentation. By taking into account the object boundaries and the inner-homogeneity, the superpixel-level guidance image leads to the geometrical information of land-cover objects in VHR images being better depicted. High-dimensional multi-scale guided filter (MSGF) features are then generated, where the multi-scale information of those land-cover classes is better modelled. In addition, for improving the computational efficiency without the loss of accuracy, a subset of those MSGF features is then automatically selected by using an unsupervised feature selection method, which contains the most distinctive information in all constructed MSGF features. Quantitative and qualitative classification results obtained on two QuickBird remotely sensed imagery datasets covering the Zurich urban scene are provided and analyzed, which demonstrate that the proposed methods outperform the state-of-the-art reference techniques in terms of higher classification accuracies and higher computational efficiency.

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