Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

Wavelet-Based Multiscale Adaptive LBP with Directional Statistical Features for Recognizing Artificial Faces

Recognizing avatar faces is a very important issue for the security of virtual worlds. In this paper, a novel face recognition technique based on the wavelet transform and the multiscale representation of the adaptive local binary pattern (ALBP) with directional statistical features is proposed to increase the accuracy rate of recognizing avatars in different virtual worlds. The proposed technique consists of three stages: preprocessing, feature extraction, and recognition. In the preprocessing and feature extraction stages, wavelet decomposition is used to enhance the common features of the same subject of images and the multiscale ALBP (MALBP) is used to extract representative features from each facial image. Then, in the recognition stage the wavelet MALBP (WMALBP) histogram dissimilarity with statistical features of each test image and each class model is used within the nearest neighbor classifier to improve the classification accuracy of the WMALBP. Experiments conducted on two virtual world avatar face image datasets show that our technique performs better than LBP, PCA, multiscale local binary pattern, ALBP, and ALBP with directional statistical features (ALBPF) in terms of the accuracy and the time required to classify each facial image to its subject.