Targeted Land-Cover Classification
نویسندگان
چکیده
منابع مشابه
Sequential Land Cover Classification
Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research ...
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Zhe Jiang, [email protected] Abstract: My research explores novel computational techniques to map the physical cover (e.g., forests) of the earth’s surface from satellite images. Processing these images is labor-intensive and a significant burden on scientists. Existing methods ignore spatial information and assume that pixels are statistically independent. Consequently, they produce erroneous map...
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From the past epoch, there have been great hard work and researches are undertaken for mounting land-cover classification in data mining. Whereas the essential uses of the remotely-sensed images is for mainly land-cover classification, for the purpose of applications like monitoring the forest and agricultural areas. Through remote sensing technology an extensive diversity of digital imagery is...
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Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2014
ISSN: 0196-2892,1558-0644
DOI: 10.1109/tgrs.2013.2280150