Study on Application of Multi-Spectral Image Texture to Discriminating Rice Categories Based on Wavelet Packet and Support Vector Machine
- 物理技术－已发表论文 
Based on multi-spectral digital image texture feature, a new rapid and nondestructive method for discriminating rice categories was put forward. The new method combined the advantages of wavelet packet and support vector machine (SVM). In the present study, the images which are 1 036 pixels in vertical direction by 1 384 pixels in horizontal direction with 24-bit depth were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multi-spectral digital imager. The three wavebands of image (red, green and NIR) can be composed into one image which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. NIR waveband images were decomposed to 16 subbands using three wavelet packet multi-resolution. Because the main feature of texture information is concentrated on the middle frequency, the 8 subbands of middle frequency were selected to calculate entropy, and the entropy of three wavebands of original image was calculated at the same time. Eighty images (twenty for each category) were used for calibration set and eighty images (twenty for each category) were used as the prediction set. Then the rice categories were classified by SVM. The classification rate of rice categories was only 93. 75% using the entropy of original image, but reached 100% by wavelet packet decomposition. The overall results show that the technique combining wavelet packet and support vector machine can be efficiently utilized for texture recognition of multi-spectra, and is an effective and simple technique for discriminating the rice categories. This study also provides a foundation for rice grading and other rice industry processing such as quality diction and milling degree.