Historic land cover change in the agricultural Midwest using an object-based approach for classification of high-resolution imagery

2013 ◽  
Vol 7 (1) ◽  
pp. 073506 ◽  
Author(s):  
Sarah Porter ◽  
Marc Linderman
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Change ◽  
High Resolution Imagery ◽  
Object Based

scholarly journals Performance of Combination of Texture and Object Based Techniques in Image Classification for Urban Land Cover

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
J. Jacinth Jennifer
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Technology Development ◽  
Urban Land ◽  
Urban Land Cover ◽  
High Resolution Imagery ◽  
Object Based ◽  
Texture Parameters

<div><p class="IJARCSAbstract"><em>Satellite imagery paves way to obtain tangible information through remote sensing techniques.  It is necessary to classify the image in order to extract the features.  There exist various classification techniques and algorithms to retrieve various features from imagery.  As the technology development proceeds in a faster track it is necessary to compensate its advancements by developing new techniques for feature retrieval.  As far as high resolution satellite imagery are concerned object based feature retrieval and texture based feature retrieval techniques are gaining its importance.  The texture based feature retrieval has various techniques involved in it, among which Haralick’s texture parameters has much importance.  Thereby object based technique also has its own way of algorithms and processes for feature retrieval.  The eCognition software provides a platform for combining texture and object based technique.  It is well known from various journals that object based technique is best for classifying high resolution imagery.  Thus the image is primarily segmented into objects for classification.  The Haralick’s texture parameters which serve well in classification of urban land cover is chosen by computing statistical analysis.  Finally the chosen texture parameter is adopted in the classification of the objects.  The classified imagery is checked for accuracy and a high accuracy of 94.5% is obtained.</em></p></div>

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 Locating and dating land cover change events in the Renosterveld, a Critically Endangered shrubland ecosystem

2020 ◽  
Author(s):  
Glenn R. Moncrieff
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Temporal Frequency ◽  
Critically Endangered ◽  
High Resolution Imagery ◽  
Change Events ◽  
Natural Land ◽  
Very High Resolution Imagery ◽  
Forested Ecosystems

Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodiverse, Critically Endangered, shrubland ecosystem - the Renosterveld of South Africa. Using supervised classification of Sentinel 2 data, and subsequent manual verification with very high resolution imagery, I locate all conversion of Renosterveld to non-natural land cover between 2016 and 2020. Land cover change events are further assigned dates using high temporal frequency data from Planet labs. 478.6 hectares of Renosterveld loss was observed over this period, accounting for 0.72 % of the remaining natural vegetation in the region. 50% of change events were dated to within two weeks of their actual occurrence, and 87% to within two months. Change often preceded the planting and harvesting seasons of rainfed annual grains. These results show the potential for new satellite platforms to accurately map land cover change in non-forest ecosystems, and detect change within days of its occurrence. There is potential to use this and similar datasets to automate the process of change detection and monitor change continuously.

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

2012 ◽  
Vol 33 (19) ◽  
pp. 5973-5995 ◽  
Author(s):  
Carolina Moutinho Duque de Pinho ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Cláudia Maria de Almeida ◽  
Hermann Johann Heinrich Kux
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Environment ◽  
Land Cover Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
High Resolution Images
Sign in / Sign up

Export Citation Format

Share Document