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| A Novel Method for High-Order Residual Quantization-Based Spectral Binary Coding |
| KANG Xiao-yan1,2, ZHANG Ai-wu1,2* |
1. Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
2. Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China |
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Abstract Spectral binary coding and multivalued coding technology can make objects spectra match, identify, and classify fast; but this kind of quantization coding methods will lose a lot of spectral details, and they cannot decode the reconstructed spectra similar to the original spectra. So they were only used for coarse horizontal applications in the past, such as rough classification. For resolving the above problems, a new spectral coding method, namely, HOBC (High-Order Binary Coding) was proposed based on high-order residual quantization. First, the original spectra were standardized by subtracting their own vector-mean, and thus the spectral sequences with a range of (-1, 1) were obtained. Second, the code with -1 and 1, its coding coefficient, and the residual (i. e., the first order residual) of a normalized spectrum were computed. Third, the binary codes with ±1 and their coding coefficients of the residuals from Two-Order to K-Order were computed order by order. At last, the K coding sequences and their corresponding coefficients were obtained. Using a typical spectral library dataset, spectral quantization encoding and decoding reconstruction experiments were carried out, compared with BC01 (spectral Binary Coding with 0 and 1), SPAM (SPectral Analysis Manager), a binary/quaternary hybrid coding, namely SDFC (Spectral Derivative Feature Coding), and a quaternary coding, namely DNA. During the experiments, first, the information entropy and memory storage of spectral shape feature and slope feature were calculated, respectively. Second, the spectral vector distance (SVD), spectral correlation coefficient (SCC), and spectral angle mapping (SAM) between the spectral shape feature and the original spectrum were calculated. The results of above experiments demonstrate that, on coding memory storage, HOBC 1—4 order encodings are equal to BC01, SPAM, SDFC, and DNA, respectively; on coding information entropy, HOBC 1—2 order encodings are equal to BC01 and SPAM, respectively, but HOBC 3—4 order encodings are higher than SDFC and DNA, respectively; on SCC, HOBC one order encoding is equal to BC01, but HOBC 2—4 order encodings are better than SPAM, SDFC, and DNA, respectively; on SAM, HOBC 1—4 order encodings are superior to the above four methods obviously, respectively; the four methods cannot be explicitly decoded and reconstructed, but it is easy to reconstruct the decoding sequence similar to the original spectrum for HOBC, and the SVDs of the reconstruct spectra are diminishing from a lower order to a higher order. Furthermore, the spectral coding and supervised classification experiments of 10 types of ground objects were carried out on the open spectral dataset of the Linze grassland foci experimental area. Results show that, on the three performance evaluation indices, i. e., Kappa value, overall classification accuracy, and average classification accuracy, HOBC is superior to the four coding methods. Especially, the classification performance of HOBC 4-order encoding is better than that of the original spectra. For the objects difficult to classify with small-sample and high similarity between classes, HOBC is also superior to other methods, and it is more robust. Therefore, first, HOBC can dramatically compress data. Meanwhile, its coding sequence can retain more information and have higher spectral separability, which can be used for fast identification and classification of spectra with high accuracy. At last, its decoding reconstruct data can also be used for the applications of target recognition and classification etc., theoretically, for the high similarity between the reconstruction spectra and original spectra.
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Received: 2018-08-27
Accepted: 2019-02-10
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Corresponding Authors:
ZHANG Ai-wu
E-mail: zhangaw98@163.com
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