The conventional singular spectrum analysis is to divide a signal into segments where there is only one non-overlapping point between two consecutive segments. By putting these segments into the columns of a matrix and performing the singular value decomposition on the matrix, various one dimensional singular spectrum analysis vectors are obtained. Since different one dimensional singular spectrum analysis vectors represent different parts of the signal such as the trend part, the oscillation part and the noise part of the signal, the singular spectrum analysis plays a very important role in the nonlinear and adaptive signal analysis. However, as the length of each one dimensional singular spectrum analysis vector is the same as that of the original signal, there is a redundancy among these one dimensional singular spectrum analysis vectors. In order to reduce the required computational power and the required units for the memory storage for performing the singular spectrum analysis, this paper proposes a method to reduce the total number of the elements of all the one dimensional singular spectrum analysis vectors. In particular, the length of the shift block for generating the trajectory matrix is increased from one to a positive integer greater than one under a certain criterion. In this case, the total number of the columns of the trajectory matrix is reduced. As a result, the total number of the off-diagonals of all the two dimensional singular spectrum analysis matrices is reduced. Hence, the total number of the elements of all the one dimensional singular spectrum analysis vectors is reduced. In order to guarantee exact perfect reconstruction, this paper reformulates the de-Hankelization process. In particular, the first element of the off-diagonals of all the two dimensional singular spectrum analysis matrices is taken as the elements of the one dimensional singular spectrum analysis vectors. Exact perfect reconstruction condition is derived. Simulations show that exact perfect reconstruction can be achieved while the total number of the elements of all the one dimensional singular spectrum analysis vectors is significantly reduced.

Because conventional PCANET approach is that the conventional PCANET performs the PCA for all the segments of all the training pixel vectors, and this does not capture the difference between different segments of the same training pixel vectors, classification accuracy is not high. This paper proposes to employ a regional principal component analysis network with the rolling guidance filter (RPCANET_RGF) for performing the hyperspectral image (HSI) classification with few training samples. Regional principal component analysis network (RPCANET) proposed in this paper performs the PCA for each segment of all training pixel vectors. Besides, the rolling guidance filter (RGF) is used to remove the spatial noise and to enhance the edges of the HSIs. Different from the conventional convolutional neural networks (CNNs), the coefficients of the filters are obtained by performing the principal component analysis (PCA) on the regional segments of HSIs. This approach is also different from the conventional principal component analysis network (PCANET). Here, different segments of the same pixel image are processed by different Filters. Since the RPCANET_RGF is a general learning method that obtains the filter coefficients directly from the HSIs, the back propagation based training is not required. Hence, the RPCANET_RGF requires a less computational power for performing the training compared to the CNN. Besides, as the RPCANET_RGF can make use of both the spectral information and the spatial information for performing the classification, the computer numerical simulation results show that the classification accuracy achieved by the RPCANET_RGF is higher than that by the conventional PCANET and other state of the art methods. INDEX TERMS Regional principal component analysis network, rolling guidance filter, hyperspectral image, classification.

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Anemia is one of the most widespread clinical symptoms all over the world, which could bring adverse effects on people's daily life and work. Considering the universality of anemia detection and the inconvenience of traditional blood testing methods, many deep learning detection methods based on image recognition have been developed in recent years, including the methods of anemia detection with individuals’ images of conjunctiva. However, existing methods using one single conjunctiva image could not reach comparable accuracy in anemia detection in many real-world application scenarios. To enhance intelligent anemia detection using conjunctiva images, we proposed a new algorithmic framework which could make full use of the data information contained in the image. To be concrete, we proposed to fully explore the global and local information in the image, and adopted a two-branch neural network architecture to unify the information of these two aspects. Compared with the existing methods, our method can fully explore the information contained in a single conjunctiva image and achieve more reliable anemia detection effect. Compared with other existing methods, the experimental results verified the effectiveness of the new algorithm.