Multispectral Sensing of Satellite Images for the Classification of Different Land Covering Area by Support Vector Machine-2 Method
نویسندگان
چکیده
The problem of scarcity of multi image supporting for satellite image is handled with the help of multispectral sensing image method. In that the SVM-2 (Support Vector Machine – 2) is consider for classifying the different types of land covering area. The workflow of multispectral acquired with an absurd image into orthorectified image, in that we identified several challenges including file format compatibilities and a size of the original image. These compatibilities are handled with the help of segmentation and semi supervised learning algorithm. The segmentation is used to simplify the portrayal of large image into something more meaningful and easier to analyze. The Multispectral provides a quality and high resolution information for satellite sensor applications. With the help of semi supervised learning algorithm and multispectral sensing image the overall performance of PSNR is increased upto 42.98%.
منابع مشابه
Remote Sensing and Land Use Extraction for Kernel Functions Analysis by Support Vector Machines with ASTER Multispectral Imagery
Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an effic...
متن کاملDistribution map of the different lithologic units in loess plateau of eastern Golestan by using remote sensing technique; Aghband research area
Introduction: Along with the climate, Soil is an essential natural resource. Although soil studies in Iran have been started more than 50 years ago, the soil map of the country has not been fully prepared yet, and only 20-25% of the lands have been mapped already. Many soil maps of Iran need to be updated, but the common methods in soil mapping are costly and time-consuming. Hence, using data o...
متن کاملAdvanced machine learning methods for wind erosion monitoring in southern Iran
Extended abstract Introduction Wind erosion is one the most important factors of land degradation in the arid and semi-arid areas and it is one the most serious environmental problems in the world. In Fars province, 17 cities are prone to wind erosion and are considered as critical zones of wind erosion. One of the most important factors in soil wind erosion is land use/cover change. T...
متن کاملComparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran
The objective of this research was to determine the best model and compare performances in terms of producing landuse maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance ofmean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral anglemapper (SAM), and support vector machine (SVM) were...
متن کاملAutomatic Interpretation of UltraCam Imagery by Combination of Support Vector Machine and Knowledge-based Systems
With the development of digital sensors, an increasing number of high-resolution images are available. Interpretation of these images is not possible manually, which necessitates seeking for practical, fast and automatic solutions to solve the environmental and location-based management problems. The land cover classification using high-resolution imagery is a difficult process because of the c...
متن کامل