Comparing pixel based and object based approaches in land use classification in mountainous areas (original) (raw)
Related papers
2014
Optimal natural resources management depends on reliable as well as up-to-date data. For this objective, land use map is one of important source of information on the natural resources management. The aim of present study is to compare two methods of pixel-Base and objectoriented classification in land use mapping with using ETM+ image in Malekshahi town, Ilam province. After the supply of related image and geometric and radiometric corrections implement on image, we use of tow method classification to land use mapping. To assess the accuracy of classification method we used index of overall accuracy, kappa coefficient, producer accuracy and user accuracy. The results show that the object-oriented classification method has more resolution than the pixel-based classification method. The results of determine accuracy show that method of object-oriented in tow index of overall accuracy and kappa coefficient with (respectively) 96 percent and 93 percent was more accuracy related to pixel-based classification method. Based on the results the forest and grassland covers about 98 percent of this town. The result of this study suggested that used from object-oriented classification method to production of land use map.
Geomatics and Environmental Engineering
Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater th...
International Journal of Environment and Geoinformatics
There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes with no mixed pixels, and then qualitatively into educational, commercial, health, residential, and security land use classes that were conflicting due to spectral similarity. The total accuracy and kappa coefficient of the pixel-based land cover classification were 92.5% and 94% respectively. The results showed that residential land use occupied an area of 5500.01ha, followed by educational (280...
Abstract: Automated features extraction techniques have gone a long way to ameliorate pains on imageprocessing and information extraction from remotely sensed image; this has opened many doors for manyapplications. However, pixel based image classification and feature extraction of Nigerian urban areas are stillvery difficult due to the spectral similarity of many of the urban features such as roads, bare ground, rockoutcrop, built-up areas and many more. This is as a result of the nature of the roads (untarred) and otherfeatures like built-up areas with large area coverage of bare ground rather than well landscaped with grassgardens. This makes high classification accuracy of urban feature extraction very difficult to achieve. Theperformance of urban feature extraction from LANDSAT imagery of Abuja, Nigeria with eCognition isoverwhelming. This research compared the result of object oriented image classification (i.e. eCognition) andother traditional (i.e. Erdas Imagine) methods of image classification vis-à-vis urban feature extraction frommedium resolution remotely sensed image of Abuja Nigeria which has a very highly similar spectral reflectancevalue. The result shows overall accuracy of 94.9% as against the overall accuracy of 89% in using thetraditional method in Erdas Imagine. While the ever difficult and very confused class differentiation in imageclassification in Nigerian urban areas between built-up and bare ground was clearly separated with high levelof accuracy Keyword: Pixel Based Classification, Object-Oriented Classification, Automatic Feature Extraction, eCognition.
Comparison of methods for land-use classification incorporating remote sensing and GIS inputs
Applied Geography, 2011
Over the last few decades, dramatic land-use changes have occurred throughout Israel. Previously-grazed areas have been afforested, converted to irrigated or rain-fed agriculture, turned into natural reserves, often used as large military training sites, converted to rural and urban settlements, or left unused. Landuse maps provided by the Israeli governmental are more detailed for agricultural and urban land-use classes than for others. While rangelands still account for a substantial part of the northern Negev, their extent today is not well defined. In light of continuous land-use changes and lack of regard to rangelands in existing land-use maps, there is a need for creating a current land-use information database, to be utilized by planners, scientists, and decision makers. Remote-sensing (RS) data are a viable source of data from which land-use maps could be created and updated efficiently. The purpose of this work is to explore low-cost techniques for combining current satellite RS data together with data from the Israeli Geographic Information System (GIS) in order to create a relatively accurate and current land-use map for the northern Negev. Several established methods for land-use classification from RS data were compared. In addition, ancillary land-use data were used to update and improve the RS classification accuracy within a GIS framework. It was found that using a combination of supervised and unsupervised training classes produces a more accurate product than when using either of them separately. It was also found that updating this product using ancillary data and GIS techniques can improve the product accuracy by up to 10%. The final product's overall accuracy was 81%. It is suggested that applying the presented technique for more RS images taken at different times can facilitate the creation of a database for land-use changes.
Journal of Mountain Science, 2013
： Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS (Advanced Land Observing Satellite) in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures (such as shadow). At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM (Gray Level Cooccurrence Matrix) were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification (MLC). Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and 0.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.
Comparative Assessment Between Per-Pixel and Object-Oriented for Mapping Land Cover and Use
Engenharia Agrícola
The traditional per-pixel classification methods consider only spectral information, and may be limited. Object-based classifiers, however, also consider shape and texture, firstly segmenting the image, and then classifying individual objects. Thus, a Geographic Object-Based Image Analysis (GEOBIA) was compared in conjunction with data mining techniques and a traditional per-pixel method. A cut of Landsat-8, bands 2 to 7, orbit/point 223/77, located between the municipalities of Cascavel, Corbélia, Cafelândia and Tupãssi, in the west part of the state of Paraná, from 12/18/2013 was used. In the GEOBIA approach was realized image segmentation, spatial and spectral attribute extraction, and classification using the decision tree supervised algorithm, J48. For the per-pixel method, we used the supervised Maximum Likelihood Classifier. Both approaches presented equivalent results, with Kappa Index of 0.75 and Global Accuracy (GA) of 78.97% for the approach by GEOBIA and Kappa Index of 0.72 and GA of 77.44% for the perpixel classification. The classification by GEOBIA showed better accuracy for the soil, forest and soybean classes, and did not show the splash aspect, which visually improves the classification result.
Image Processing, Signal Processing, and Synthetic Aperture Radar for Remote Sensing, 1997
An object{based approach for producing land use maps will be described in this paper. This approach has been used for integrating Landsat TM data within a GIS context for producing land use maps of urban{rural fringe areas. A contextual image classi cation method based on the SMAP estimate was used to produced land cover maps which provide knowledge for inferring land use types. Objectised land cover information, thematic knowledge and spatial composition rules were used to infer the land use type of each object area. The prototype of this approach has been built using the GRASS 4.1 GIS software package and tested using a dataset compiled for this purpose. Results indicate a signi cant improvement compared with land use maps produced using a contextual image classi cation approach alone.
South African Journal of Geomatics, 2017
In the past, large scale mapping was carried using precise ground survey methods. Later, paradigm shift in data collection using medium to low resolution and, recently, high resolution images brought to bear the problem of accurate data analysis and fitness-for-purpose challenges. Using high resolution satellite images such as QuickBird and IKONOS are now preferred alternatives. This paper is aimed at comparing pixel-based (PIXBIA) and Geo-object-based (GEOBIA) classification methods using ENVI 4.8 and eCongnition software respectively, and ArcGIS 10.1 for map layout creation. It uses Aba main city in southeastern Nigeria as a case study. The paper further evaluates the classification accuracies obtained using error matrix and then test the classifications' agreement to geographic reality using Kappa Coefficient statistical analysis. Analyzing 2012 QuickBird image as a proof of concept, the study shows that the objectbased approach had a higher overall accuracy (OA= 98.75%) than the pixel-based approach (OA=79.44%). With a Kappa Coefficient of K=0.97 (very good) for object-based approach and K=0.62 (good) for pixel-based, the object-based method showed a higher class separability between and among examined geographic objects such as water, bare-land and tree canopy as evidenced in the Golf Course under reconstruction in Aba city. In addition, the object-based results also show a higher overall producer accuracy (PA=98.42% > PA=85.37) and user accuracy (UA=96.70 > UA=81.04%) respectively. The paper, therefore, recommends that objectbased classification method be applied in analyzing high resolution satellite image. The approach is also recommended for mapping urban areas in developing countries such as Nigeria where the paucity of fund required in flying airplane for the production of orthophotos is a major challenge in large scale mapping.
Land use land cover analysis with pixel-based classification approach
Indonesian Journal of Electrical Engineering and Computer Science, 2019
Rapid development in certain urban area will affect its natural features. Therefore, it is important to identify and determine the changes occur for further analysis and future development planning. This process will influence several factors such as area development, environmental issues and human social activities. The selection of remote sensing data and method will derive the accurate land use land cover maps. This research study accessed the classification accuracy of different classifier approach for land use land cover classification in urban area. The objective of this paper is to compare the accuracy of the classification for each technique used. The study was conducted in a highly urbanized area in Kuala Lumpur, Malaysia. The dataset used for this study is the multi temporal LANDSAT satellite imageries for the year of 2001,2006,2011 and 2016. The pre-processing and analysis of the dataset has been done using software ENVI 5.3. Five land use classes (Urban/built up area, Fo...