Apport de l'information mutuelle pour la sélection d'attributs et la classification des images hyperspectrales

Hyperspectral images (HSI) classification is a high method for remote sensing to reproduce different thematic maps . The HSI contains more than a hundred bidirectional measures called bands (or simply images), of the same region called Ground Truth (GT). They are taken at juxtaposed frequencies. Unfortunately, some bands are redundant, others are noisily measured, and the high dimensionality of features madkes the classification accuracy poor. The problematic is how to find the good bands to classify the region points. Some methods use Mutual Information (MI) and thresholding, to select only relevant images. Others control and avoid redundancy. But they process the dimensionality reduction, some times as selection, other times as extraction methods without any relationship. Here , we introduce a survey on all schemes used, and we synthesize a dashboard, that helps user to study selection and extraction features. Fristly, We establish this study on HSI AVIRIS 92AV3C, and the classifier used is SVM (Support Voctor Machine). There are some methods to control and eliminate redundancy by selecting the band top ranking the MI, and if its neighbors have sensibly the same MI with the GT, they will be considered redundant and so discarded. This is the most inconvenient of this method, so some precious information can be discarded. In the second method that we introduce, we accept the useful redundancy : a band contains useful redundancy if it contributes to produce an estimated reference map that has a higher MI value with the GT. To control redundancy, we introduce a complementary threshold added to last value of MI. This process is a Filter strategy ; it gets a better performance of classification accuracy and not expensive, but less preferment than wrapper strategy. In the third step, we note that others studies use the MI’s normalized forms, like Symmetric Uncertainty, in medical imagery applications. So we introduce an algorithm based also on MI to select relevant bands and it apply the symmetric uncertainty coefficient to control redundancy and increase the accuracy of classification. This algorithm is a feature selection tool and a wrapper strategy. This is an effectiveness scheme to control redundancy. Then we use two other forms of MI, they give, pratically the same performence. We conclude the effectiveness of this method and algorithm to select the relevant and no redundant bands. In the forth step, we design an algorithm based also on MI combined with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy, and select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. In this schema we had to clear the difference of the result’s algorithm and the human decision, and this can be viewed as a case study which human decision is different to an intelligent algorithm. Finally, we introduce a new scheme using also mutual information, but it retains only the bands minimizing the error probability of classification. To control redundancy, also we introduce a complementary threshold. So the good band candidate must contribute to decrease the last error probability augmented by the threshold. This process is a wrapper strategy ; it gets high performance of classification accuracy but it is expensive than filter strategy. Keyword : Hyperspectral images, Classification, Feature selection, Mutual Information, Normalized Mutual information, Relevence, Redundancy, Error Probability, Remote Sensing.