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- W2607398131 abstract "Object-Oriented Image Classification method is a useful and promising method of classifying objects from high resolution satellite images. The method segments the image pixel into objects and utilizes the texture and contexture information of the object rather than only using spectral information relied upon by traditional methods. This paper, using high resolution multispectral satellite imagery from WorldView-2, sought to explore ways to extract accurate trees of varying crown sizes. IMAGINE Objective tools from ERDAS IMAGINE software were used to define individual trees model parameters by employing different feature detection and extraction techniques. These enable geospatial data layers to be created and maintained through the use of remotely sensed imagery. The results show that IMAGINE Objective provides a high accuracy function for tree extraction especially when one is dealing with cluster of individual tree crowns. By using cue parameters like color, tone, orientation, texture, etc. the spectral differences between tree and others features were able to be detected. Based on training samples, the trees were quantitatively extracted by means of probability of Bayesian Network on Single Feature Probability (SFP) function. In conclusion, Object-oriented analysis proves a successful method of identifying and extracting individual trees of varying crown sizes. A high accuracy is achieved compared to other pixel-based classification techniques." @default.
- W2607398131 created "2017-04-28" @default.
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- W2607398131 date "2011-01-01" @default.
- W2607398131 modified "2023-09-27" @default.
- W2607398131 title "OBJECT-ORIENTED IMAGE CLASSIFICATION OF INDIVIDUAL TREES USING ERDAS IMAGINE OBJECTIVE: CASE STUDY OF WANJOHI AREA, LAKE NAIVASHA BASIN, KENYA" @default.
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