Mohammad Ghasem Torkashvand; Mostafa Mousapour
Abstract
Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate ...
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Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces hasspecial importancefor the perspective of climatic, ecologic and hydrologic studies. The researchers believe that remote sensing data can lead to better assess from the snow-covered areas than traditional topography methods. Therefore, nowadays, in efficient management of water resources, remote sensing data aims to achieve exact information on snow-covered areasis applying operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.
Material & Methods
The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machinethat is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis can be done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space, so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combining the object-oriented and Fuzzy methods provides the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class, so according to the membership degree, image piece classification is done leading to the increased final precision.
Results & Discussion
In this research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points, so based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radialand sigmoid Cornell functions were applied,soclassification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software, then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process, for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. The training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.
Conclusion
The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is the highest accuracy (98%) and to classify digital images,the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.
Mohammad Hossein Rezaei Moghaddam; Keyvan Mohammadzade; Majid Pishnamaz Ahmadi
Abstract
Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of ...
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Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring.Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies.
Materials and methods
A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery).
Discussion and results
Different functions used in OBIA techniques,such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies.
Conclusion
In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.
Sayyad asghari Saraskanrood; behrooz khodabandelo; Ahmad Naseri; Ali moradi
Abstract
Extended Abstract Introduction Currently, two general methods are used for classification of digital satellite images: pixel-based and object-oriented processing. Unlike pixel-based Methods, object-oriented techniques employ different geometric, spatial, spectral, and form-based algorithms, and selecting ...
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Extended Abstract Introduction Currently, two general methods are used for classification of digital satellite images: pixel-based and object-oriented processing. Unlike pixel-based Methods, object-oriented techniques employ different geometric, spatial, spectral, and form-based algorithms, and selecting the most efficient algorithm in this process requires a lot of experience in image processing. In addition, multiple algorithms usually offer different results and this in many cases makes the selection of efficient algorithms difficult. In general, pixel-based classification includes supervised and unsupervised methods. Examples of these methods include maximum likelihood, neural network and support vector machine. Maximum likelihood method is one of the most effective methods used for image classification. Object-oriented methods take advantage of knowledge-based algorithms, and thus overcome problems pixel-based method faces because of not using geometric and textual information. In order to achieve high classification accuracy, two methods of pixel-based and object-oriented classification are compared in this research. On the one hand, integrated planning and management of urban areas, and on the other hand, collecting reliable information regarding land use makes this kinds of studies indispensable. Materials&Methods Present study seeks to extract urban land use map. Thus, necessary data was received from Sentinel-2. Moreover, ENVI 5.3, eCognation 9, SNAP, ArcGIS 10.3, Google Earth, and land-use data were also used to process images and analyze data. In SNAP, atmospheric correction process was performed on images collected from the study area using SEN2COR plug-in. Samples collected from each class of Sentinel-2 satellite image were mapped on the image area. Pixel classification algorithms, support vector machines, maximum likelihood, artificial neural network, Minimum Distance to Mean (MDM), parallelepiped and Mahalanobis distance were used. Finally, land use classes (residential, gardens and green spaces, wastelands and passageways) in the study area were mapped using different classification algorithms. For object-oriented classification using nearest neighbor algorithm, the satellite image was first segmented in eCognation software using the Multiresolution Segmentation Algorithm. Parameters such as scale, shape and compactness were also studied in the image segmentation stage. Through trial and error, an appropriate value was selected for parameters used in segmentation. For practical comparison of the results, the same educational data was used in both object-oriented and pixel-based classification methods. Then, the most important methods for assessing accuracy including overall precision and kappa coefficient were extracted. Results & Discussion As one of the most important methods used for extracting information from remotely sensed images, classification allows users to produce various types of information such as coverage maps, and land-use maps. Classification of satellite data includes segregation of similar spectral sets and classification of sets with the same spectral behavior. Regarding the resolution of images used (10 m) in this study, only 4 land-use classes possessed the required resolution capability for pixel-based classification of Sentinel-2 satellite images. These classes include built-up (residential) area, waste land, urban green space and street network. In this regard, support vector machine, maximum likelihood, artificial neural network, Minimum Distance to Mean, parallelepiped and Mahalanobis distance were used for classification. Classification results indicate that compared to other pixel-based methods, maximum likelihood method and Minimum Distance to Mean method show a precision of 85% or higher. In present study, geometric properties of land use classes (including scale, shape, and compactness) were used for segmentation and this process was performed by multiresolution method. For this purpose, results of image segmentation process were analyzed based on different parameters (with different scales) and spatial resolution of the image. In this way, appropriate values for segmentation were selected based on the specific features of the study area (an urban environment) through trial and error. Then, the proper image segmentation was selected and prepared for the classification stage using the above mentioned parameters. In the next step, 20 effective parameters including statistical indices, mean score of bands, NDVI index, standard deviation of the bands and geometric index were used for classification. Conclusion The present study took advantage of six pixel-based methods (Support Vector Machine, Maximum Likelihood, Neural Network, Minimum Distance to Mean, Parallelepiped, and Mahalanobis) along with object-oriented classification method to produce a land-use map for Zanjan city. The accuracy of classification in different methods were compared and statistically analyzed using overall accuracy coefficient, kappa coefficient, user’s accuracy, and producer’s accuracy. The results of statistical analysis of the accuracy coefficients indicated that Minimum Distance to Mean and Maximum Likelihood method -with a Kappa coefficient of 90% and 85% respectively- are acceptable methods for land use mapping. Moreover, comparing pixel-based and object-oriented methods, it is possible to conclude that object-oriented approach with a Kappa coefficient of 0.95% and overall accuracy of 97.9% shows a higher potentiality. Nearest Neighbor algorithm is one of the most important reasons for achieving this high accuracy in object-oriented classification. In addition to the spectral information, this method uses information collected about issues like texture, form, position, and content for the classification process. Methods used in this study prove the accuracy of objective-oriented technique by employing effective parameters and developing rules to modify the initial classification of object-oriented technique. Another advantage of object-oriented method (as compared to pixel-based methods) is that apart from spectral information and statistical data, it is possible to apply several other indicators such as shape, texture, color, dimensions and altitude of the phenomena in the final land use map produced by this method. Finally, it should be noted that object-oriented classification has been developed for high resolution spatial data.
