Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of fast growing data archives, with continuous changes in volume and diversity, information mining has proven to be a difficult, yet highly recommended task. The first and perhaps the most important part of the process is data representation for efficient and reliable image classification.  is presenting a new approach for describing the content of Earth Observation Very High Resolution images, by comparison with traditional representations based on specific features. The benefit of data compression is exploited in order to express the scene content in terms of dictionaries. The image is represented as a distribution of recurrent patterns, removing redundant information, but keeping all the explicit features, like spectral, texture and context. Further, a data domain analysis is performed using Support Vector Machine aiming to compare the influence of data representation to semantic scene annotation. WorldView2 data and a reference map are used for algorithm evaluation.
['Corina Văduva', 'Florin-Andrei Georgescu', 'Mihai Datcu']
I am writing a research paper on a idea Dictionary-Based Compact Data Representation for Very High Resolution Earth Observation Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Dictionary-Based Compact Data Representation for Very High Resolution Earth Observation Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of fast growing data archives, with continuous changes in volume and diversity, information mining has proven to be a difficult, yet highly recommended task. The first and perhaps the most important part of the process is data representation for efficient and reliable image classification.  is presenting a new approach for describing the content of Earth Observation Very High Resolution images, by comparison with traditional representations based on specific features. The benefit of data compression is exploited in order to express the scene content in terms of dictionaries. The image is represented as a distribution of recurrent patterns, removing redundant information, but keeping all the explicit features, like spectral, texture and context. Further, a data domain analysis is performed using Support Vector Machine aiming to compare the influence of data representation to semantic scene annotation. WorldView2 data and a reference map are used for algorithm evaluation."}]