Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification
نویسندگان
چکیده
منابع مشابه
Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification
We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC a...
متن کاملComparing the Capability of Sentinel 2 and Landsat 8 Satellite Imagery in Land Use and Land Cover Mapping Using Pixel-based and Object-based Classification Methods
Introduction: Having accurate and up-to-date information on the status of land use and land cover change is a key point to protecting natural resources, sustainable agriculture management and urban development. Preparing the land cover and land use maps with traditional methods is usually time and cost consuming. Nowadays satellite imagery provides the possibility to prepare these maps in less ...
متن کاملHistogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use.
The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated...
متن کاملSatellite Image Processing for Land Use and Land Cover Mapping
In this paper, urban growth of Bangalore region is analyzed and discussed by using multi-temporal and multi-spectral Landsat satellite images. Urban growth analysis helps in understanding the change detection of Bangalore region. The change detection is studied over a period of 39 years and the region of interest covers an area of 2182 km 2 . The main cause for urban growth is the increase in p...
متن کاملImproving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods
Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2012
ISSN: 2072-4292
DOI: 10.3390/rs4092530