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6_Object_Oriented_Classification

6_Object_Oriented_Classification - Single-pixel...

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Single Single - - pixel Classification versus pixel Classification versus Object Object - - oriented Image Segmentation oriented Image Segmentation Classification algorithms based on Classification algorithms based on single single- pixel analysis pixel analysis often are not often are not capable of extracting the information we desire from high capable of extracting the information we desire from high- spatial spatial- resolution remote sensor data (e.g., QuickBird 61 resolution remote sensor data (e.g., QuickBird 61 61 cm). For 61 cm). For example, the spectral complexity of urban land example, the spectral complexity of urban land- cover materials results cover materials results in specific limitations using per in specific limitations using per- pixel analysis for the separation of pixel analysis for the separation of human human- made materials such as roads and roofs and natural materials made materials such as roads and roofs and natural materials such as vegetation, soil, and water. Furthermore, a significant such as vegetation, soil, and water. Furthermore, a significant but but usually ignored problem with per usually ignored problem with per- pixel characterization of land cover is pixel characterization of land cover is that a substantial proportion of the signal apparently coming fr that a substantial proportion of the signal apparently coming fr om the om the land area represented by a pixel comes from the surrounding terr land area represented by a pixel comes from the surrounding terr ain. ain. Improved algorithms are needed that take into account not only t Improved algorithms are needed that take into account not only t he he spectral characteristics spectral characteristics of a single pixel but those of the surrounding of a single pixel but those of the surrounding (contextual) pixels. We need information about the (contextual) pixels. We need information about the spatial spatial characteristics of the surrounding pixels characteristics of the surrounding pixels so that we can identify areas so that we can identify areas (or segments) of pixels that are homogeneous. (or segments) of pixels that are homogeneous.
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Object-oriented Image Segmentation This need has given rise to the creation of image classification This need has given rise to the creation of image classification algorithms based on algorithms based on object object- oriented image segmentation oriented image segmentation . The . The algorithms incorporate both algorithms incorporate both spectral spectral and and spatial information spatial information in the in the image segmentation phase. The result is the creation of image segmentation phase. The result is the creation of image objects image objects defined as individual areas with defined as individual areas with shape shape and and spectral spectral homogeneity homogeneity which which one may recognize as segments or one may recognize as segments or patches patches in the landscape. In many in the landscape. In many
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