scholarly journals A review of supervised object-based land-cover image classification

2017 ◽  
Vol 130 ◽  
pp. 277-293 ◽  
Author(s):  
Lei Ma ◽  
Manchun Li ◽  
Xiaoxue Ma ◽  
Liang Cheng ◽  
Peijun Du ◽  
...  
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Cover Image ◽  
Object Based

scholarly journals Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

2013 ◽  
Vol 79 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Sory I. Toure ◽  
Douglas A. Stow ◽  
John R. Weeks ◽  
Sunil Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Curve Matching ◽  
Object Based

scholarly journals The status and decadal change of glaciers in Bhutan from the 1980s to 2010 based on satellite data

2014 ◽  
Vol 55 (66) ◽  
pp. 159-166 ◽  
Author(s):  
Samjwal Ratna Bajracharya ◽  
Sudan Bikash Maharjan ◽  
Finu Shrestha
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Satellite Data ◽  
Glacier Area ◽  
Decadal Change ◽  
Landsat Images ◽  
Equilibrium Line Altitude ◽  
Glacier Inventory ◽  
Object Based ◽  
The Status

AbstractIn order to monitor changes in the glaciers in the Bhutan Himalaya, a repeat decadal glacier inventory was carried out from Landsat images of 1977/78 (~1980), 1990, 2000 and 2010. The base map of glaciers was obtained by the object-based image classification method using the multispectral Landsat images of 2010. This method is used separately to delineate clean-ice and debris-covered glaciers with some manual editing. Glacier polygons of 2000,1990 and ~1980 were obtained by manual editing on 2010 by separately overlaying respective years. The 2010 inventory shows 885 glaciers with a total area of ~642 ± 16.1 km2. The glacier area is 1.6% of the total land cover in Bhutan. The result of a repeat inventory shows 23.3 ± 0.9% glacial area loss between ~1980 and 2010, with the highest loss (11.6 ±1.2%) between ~1980 and 1990 and the lowest (6.7 ±0.1%) between 2000 and 2010. The trend of glacier area change from the 1980s to 2010 is -6.4 ± 1.6%. Loss of glacier area was mostly observed below 5600 m a.s.l. and was greater for clean-ice glaciers. The equilibrium-line altitude has shifted upward from 5170 ± 110 m a.s.l. to 5350 ± 150 m a.s.l. in the years ~1980-2010.

scholarly journals LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 189-193
Author(s):  
D. Rawal ◽  
A. Chhabra ◽  
M. Pandya ◽  
A. Vyas
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Spectral Reflectance ◽  
Land Cover Classification ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Classification Technique ◽  
Object Based

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

Object-Oriented Land Cover Image Classification System

2010 ◽  
Vol 4 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Yun Yongsheng ◽  
Chang Qingrui ◽  
Xie Jing
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Classification System ◽  
Object Oriented ◽  
Cover Image

scholarly journals Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 1222-1227
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Image Classification ◽  
Water Body ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Object Based

The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.

A neuro-fuzzy multilayered classifier for land cover image classification

2007 ◽  
Author(s):  
N.E. Mitrakis ◽  
C.A. Topaloglou ◽  
T.K. Alexandridis ◽  
J.B. Theocharis ◽  
G.C. Zalidis
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Cover Image ◽  
Neuro Fuzzy
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