Differences of image classification techniques for land use and land cover classification

2015 ◽  
Author(s):  
Nur Anis Mahmon ◽  
Norsuzila Ya'acob ◽  
Azita Laily Yusof
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Classification ◽  
Classification Techniques

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.

scholarly journals Land Use and Land Cover Classification from ETM Sensor Data : A Case Study from Tamakoshi River Basin of Nepal

2014 ◽  
Vol XL-8 ◽  
pp. 943-948
Author(s):  
U. S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Classification Methods ◽  
Barren Land ◽  
Grass Land ◽  
Mountain Watershed

The mountain watershed of Nepal is highly rugged, inaccessible and difficult for acquiring field data. The application of ETM sensor Data Sat satellite image of 30 meter pixel resolutions has been used for land use and land cover classification of Tamakoshi River Basin (TRB) of Nepal. The paper tries to examine the strength of image classification methods in derivation of land use and land classification. Supervised digital image classification techniques was used for examination the thematic classification. Field verification, Google earth image, aerial photographs, topographical sheet and GPS locations were used for land use and land cover type classification, selecting training samples and assessing accuracy of classification results. Six major land use and land cover types: forest land, water bodies, bush/grass land, barren land, snow land and agricultural land was extracted using the method. Moreover, there is spatial variation of statistics of classified land uses and land cover types depending upon the classification methods. <br><br> The image data revealed that the major portion of the surface area is covered by unclassified bush and grass land covering 34.62 per cent followed by barren land (28 per cent). The knowledge derived from supervised classification was applied for the study. The result based on the field survey of the area during July 2014 also verifies the same result. So image classification is found more reliable in land use and land cover classification of mountain watershed of Nepal.

scholarly journals Innovative approach for automatic land cover information extraction from LiDAR data

2021 ◽  
Author(s):  
Nagwa Taha Hamdy El-Ashmawy
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Image Classification ◽  
Point Cloud ◽  
Land Cover Classification ◽  
Large Datasets ◽  
Lidar Data ◽  
Intensity Data ◽  
Classification Techniques ◽  
Change Curve

An airborne laser scanning (ALS) system with LiDAR (Light Detection and Ranging) technology is a highly precise and accurate 3D point data acquisition technique. LiDAR technology has been extensively used in digital surface/terrain modelling (DSM/DTM), and related applications such as 3D city modelling and building extraction. The capability of LiDAR systems to record the intensity of the return laser pulse backscattered energy in addition to the range data has motivated researchers to investigate the use of LiDAR intensity data for extracting land cover information. The main goal of this research is to maximize the benefits of the use of LiDAR data independently of any external source of data for automatically extracting accurate land cover information. Several new approaches are introduced in this research: a) classifying and filling the LiDAR intensity point cloud to produce a land cover image, b) combing multiple classified data of multiple LiDAR data-strips, c) statistical analysis segmentation technique that uses the concept of the kurtosis change curve algorithm for automatic classification of LiDAR data, and d) accelerating the classification process of large datasets by partitioning the large datasets into small, manageable datasets. Applying the traditional image classification techniques on LiDAR elevation and intensity data exclusively is included. Pixel-based, object-based, and point-based classification logics are conducted, and their results are compared to reference data. The results indicated that LiDAR data (range and intensity) can independently be used in land cover classification. By applying traditional pixel-based, supervised image classification techniques, the classification results show that auxiliary layers, which are extracted from range and intensity data, can be used for land cover classification. However, applying the supervised classification techniques on the LiDAR point cloud data without converting the data into images (Point-based logic) produced more accurate land cover classification results. The experiments on the proposed classification approach using the statistical analysis segmentation technique (based on the concept of the kurtosis change curve algorithm) show that it can be used to classify LiDAR data for land cover mapping.

scholarly journals Innovative approach for automatic land cover information extraction from LiDAR data

2021 ◽  
Author(s):  
Nagwa Taha Hamdy El-Ashmawy
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Image Classification ◽  
Point Cloud ◽  
Land Cover Classification ◽  
Large Datasets ◽  
Lidar Data ◽  
Intensity Data ◽  
Classification Techniques ◽  
Change Curve

An airborne laser scanning (ALS) system with LiDAR (Light Detection and Ranging) technology is a highly precise and accurate 3D point data acquisition technique. LiDAR technology has been extensively used in digital surface/terrain modelling (DSM/DTM), and related applications such as 3D city modelling and building extraction. The capability of LiDAR systems to record the intensity of the return laser pulse backscattered energy in addition to the range data has motivated researchers to investigate the use of LiDAR intensity data for extracting land cover information. The main goal of this research is to maximize the benefits of the use of LiDAR data independently of any external source of data for automatically extracting accurate land cover information. Several new approaches are introduced in this research: a) classifying and filling the LiDAR intensity point cloud to produce a land cover image, b) combing multiple classified data of multiple LiDAR data-strips, c) statistical analysis segmentation technique that uses the concept of the kurtosis change curve algorithm for automatic classification of LiDAR data, and d) accelerating the classification process of large datasets by partitioning the large datasets into small, manageable datasets. Applying the traditional image classification techniques on LiDAR elevation and intensity data exclusively is included. Pixel-based, object-based, and point-based classification logics are conducted, and their results are compared to reference data. The results indicated that LiDAR data (range and intensity) can independently be used in land cover classification. By applying traditional pixel-based, supervised image classification techniques, the classification results show that auxiliary layers, which are extracted from range and intensity data, can be used for land cover classification. However, applying the supervised classification techniques on the LiDAR point cloud data without converting the data into images (Point-based logic) produced more accurate land cover classification results. The experiments on the proposed classification approach using the statistical analysis segmentation technique (based on the concept of the kurtosis change curve algorithm) show that it can be used to classify LiDAR data for land cover mapping.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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