Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Local information enhanced LBP

Based on the observation that there exists multiple information in a pixel neighbor,such as distance sum and gray difference sum,local information enhanced LBP(local binary pattern)approach,i.e.LE-LBP,is presented.Geometric information of the pixel neighborhood is used to compute minimum distance sum.Gray variation information is used to compute gray difference sum.Then,both the minimum distance sum and the gray difference sum are used to build a feature space.Feature spectrum of the image is computed on the feature space.Histogram computed from the feature spectrum is used to characterize the image.Compared with LBP,rotation invariant LBP,uniform LBP and LBP with local contrast,it is found that the feature spectrum image from LE-LBP contains more details,however,the feature vector is more discriminative.The retrieval precision of the system using LE-LBP is91.8%when recall is 10%for bus images.

Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB(Red,Green and Blue)image features and the corresponding moisture content of the leaf,a Canon digital camera was used to collect image information from detached leaves of heading-stage maize.A drying method was adopted to measure the moisture content of the leaf samples,and image processing technologies,including gray level co-occurrence matrices and grayscale histograms,was used to extract the maize leaf texture feature parameters and color feature parameters.The correlations of these feature parameters with moisture content were analyzed.It is found that the texture parameters of maize leaf RGB images,including contrast,correlation,entropy and energy,were not significantly correlated with moisture content.Thus,it was difficult to use these features to predict moisture content.Of the six groups of eigenvalues for the leaf color feature parameters,including mean,variance,energy,entropy,kurtosis and skewness,mean and kurtosis were found to be correlated with moisture content.Thus,these features could be used to predict the leaf moisture content.The correlation coefficient(R2)of the mean-moisture content relationship model was 0.7017,and the error of the moisture content prediction was within±2%.The R2 of the kurtosis-moisture content relationship model was 0.7175,and the error of the moisture content prediction was within±1.5%.The study results proved that RGB images of crop leaves could be used to measure moisture content.

Pulmonary Crackle Detection Based on Fractional Hilbert Transform

Crackles are an important kind of abnormal and discontinuous lung sounds,which have been found to be correlated to types of pulmonary diseases.The purpose of this work is to show a new perspective to solve the problem of crackle detection,based on an emerging theory of fractional Hilbert transform.By applying fractional Hilbert transform to lung sound signals,a two-dimension texture image can be generated.The texture features corresponding to crackles are quite easy to be extracted.Experiments illustrate the effectiveness of our method.