Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted.  look at the relationship beten hard accuracy and the uncertainty on the produced ansrs, introducing two measures based on maximum probability and α quadratic entropy. Their impact differs depending on the type of classifier. ,  deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft ansrs. Once the multiclass probability ansr vector is available for each pixel in the image,  studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft ansrs which are the best candidates for a fusion with supervised results.
['Ferdinando Giacco', 'Christian Thiel', 'Luca Pugliese', 'Silvia Scarpetta', 'Maria Marinaro']
I am writing a research paper on a idea Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs
[{"from": "human", "value": "I am writing a research paper on a idea Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted.  look at the relationship beten hard accuracy and the uncertainty on the produced ansrs, introducing two measures based on maximum probability and \u03b1 quadratic entropy. Their impact differs depending on the type of classifier. ,  deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft ansrs. Once the multiclass probability ansr vector is available for each pixel in the image,  studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft ansrs which are the best candidates for a fusion with supervised results."}]