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Classification Research of Digital Remote Sensing Image in Interpretation

Author: YaoJiYing
Tutor: MengHaiDong
School: Inner Mongolia University of Science and Technology
Course: Mining engineering
Keywords: Digital remote sensing image Image Classification Cluster analysis Incremental Clustering
CLC: TP751
Type: Master's thesis
Year: 2010
Downloads: 137
Quote: 1
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Abstract


Remote sensing technology in resource assessment, mineral resources, water resources, environmental resources play an increasingly important role . Traditional digital remote sensing image classification methods such as Maximum Likelihood Classification and minimum distance classification is based on the statistical relationship between the statistical characteristics of the training data of remote sensing image data to be classified , the classification accuracy is often less than ideal . The same time, because of the complexity of the distribution of the feature type itself , but also the reasons for the unsatisfactory classification performance of traditional classification methods . In this paper, the clustering algorithm based on digital remote sensing image interpretation classification method based on cluster analysis . First, in order to improve the complex , different shapes cluster classification results , the proposed clustering algorithm based on density and adaptive density of up to ( Clustering Algorithm Based on Density and Density reachable , CADD ) , the algorithm effectively improve the complex data clustering efficiency . Meanwhile, in order to achieve the substantial digital remote sensing image classification process , the use of incremental clustering method , combined with the CADD algorithm designed and implemented based on the sub - cluster features incremental clustering algorithm ICSCF for ( Incremental Clustering Algorithm Based on the subcluster Feature ) . Due ICSCF algorithm uses an incremental approach , theoretical analysis and experimental results show that the algorithm not only has a high clustering effect , at the same time , by images or data set partitioning , to achieve a step-by-step dynamic clustering can effectively achieve the large database cluster analysis, has good scalability and can play an important role , especially in the spatial clustering ( such as digital remote sensing image processing ) .

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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