scholarly journals TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 275-279
Author(s):  
V. Nizalapur ◽  
A. Vyas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Land Use Land Cover ◽  
Good Discrimination ◽  
Colour Composite ◽  
Single Polarization ◽  
Occurrence Matrix

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.

scholarly journals A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 665-670 ◽  
Author(s):  
K. Nivedita Priyadarshini ◽  
M. Kumar ◽  
S. A. Rahaman ◽  
S. Nitheshnirmal
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Unsupervised Classification ◽  
Sensor Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Land Use Land Cover ◽  
Sentinel 2A

<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>

scholarly journals Land use classification and land use change analysis using satellite images in Lojing, Kelantan.

2019 ◽  
Vol 7 (2) ◽  
pp. 53-60
Author(s):  
Jacqueline John Hiew ◽  
Amal Najihah M. Nor ◽  
Nur Hairunnisa Rafaai ◽  
Nur Hanisah Abdul Malek ◽  
Hasifah Abdul Aziz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Land Use Changes ◽  
Unsupervised Classification ◽  
United States Geological Survey ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Change Analysis

Remote sensing is widely used to capture the images of land use/land cover on earth. This paper studies on the land use changes in Lojing, Kelantan in 1989 dan 2006. The land use is then classified, and the classification scheme was adopted from United States Geological Survey (USGS) Land Use/ Land Cover Classification System. Supervised classification method has been used since it was proved by other research to be more accurate compared to unsupervised classification. Accuracy assessment was conducted to calculate the accuracy of the land use map produced so that at the end, a good quality of land use map is produced. The findings of this study is that, there had been an insignificant land use changes between the year 1989 and 2006. The conclusion is, Lojing had been experiencing changes in term of land use due to the increased socioeconomic activities especially agriculture and logging at the highlands of Lojing.

scholarly journals Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽  
2018 ◽  
Author(s):  
Mohamed Mostafa Mohamed ◽  
Samy Elmahdy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Confusion Matrix ◽  
Land Development ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Spectral Angle Mapper ◽  
Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

scholarly journals Land use Land Cover Mapping using Modified Ant Colony Optimization Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 100-106
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Environmental Changes ◽  
Optimization Technique ◽  
Unsupervised Classification ◽  
Ant Colony ◽  
Landsat 8 ◽  
Land Use Land Cover

Land use Land cover classification is an important aspect for managing natural resources and monitoring environmental changes. Urban expansion becomes one of the major challenges for the administrator. The LANDSAT 8 images are processed using the open source GRASS (Geographic Resource Analysis Support System). Unsupervised classification technique based on Ant Colony Optimization (ACO) algorithm has been modified and proposed as Modified Ant Colony Optimization (MACO) for LULC classification. In order to improve the classification accuracy of the proposed algorithm, we have combined spatial, spectral and texture features to extract more information of homogeneous land surface. The classification accuracy of the proposed algorithm has been compared with other unsupervised classification methods such as k-means, ISODATA and ACO algorithms. The overall classification accuracy of proposed unsupervised MACO algorithm has been increased by 11.24 %, 8.24% for open space and water bodies class, respectively as compared to ACO algorithm.

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