Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery

2014 ◽  
Vol 5 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Lei Zhang ◽  
Kun Jia ◽  
Xiaosong Li ◽  
Quanzhi Yuan ◽  
Xinfeng Zhao
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Multi Scale ◽  
High Resolution Imagery ◽  
Object Based ◽  
Segmentation Approach

scholarly journals Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

2019 ◽  
Author(s):  
Rahul Neware
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Optical Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Land Cover Mapping ◽  
High Resolution Imagery ◽  
Step Procedure ◽  
Object Based

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

scholarly journals Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

2018 ◽  
Vol 7 (11) ◽  
pp. 424 ◽  
Author(s):  
Ozgun Akcay ◽  
Emin Avsar ◽  
Melis Inalpulat ◽  
Levent Genc ◽  
Ahmet Cam
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Aerial Images ◽  
High Resolution Data ◽  
Area Of Interest ◽  
Object Based ◽  
Segmentation Parameters

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.

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