Convolutional neural networks (CNNs) have been extended to hyperspectral imagery (HSI) classification due to its better feature representation and high performance, whereas multiple feature learning has shown its effectiveness in computer vision areas. This paper proposes a novel framework that takes advantage of both CNNs and multiple feature learning to better predict the class labels for HSI pixels. We built a novel CNN architecture with various features extracted from the raw imagery as input. The network generates the corresponding relevant feature maps for the input, and the generated feature maps are fed into a concatenating layer to form a joint feature map. The obtained joint feature map is then input to the subsequent layers to predict the final labels for each hyperspectral pixel. The proposed method not only takes advantage of enhanced feature extraction from CNNs, but also fully exploits the spectral and spatial information jointly. The effectiveness of the proposed method is tested with three benchmark data sets, and the results show that the CNN-based multi-feature learning framework improves the classification accuracy significantly.

Spatial information has been demonstrated to be useful for hyperspectral images (HSIs) classification. The challenge is that spatial properties are often present at various spatial scales instead of a single fixed scale. A multiscale conservative smoothing algorithm is proposed in this paper to reduce noise and extract spatial structure information from coarse to fine levels. Over-smoothing is prevented automatically by imposing a weighting scheme on the neighboring pixels used for smoothing, where dissimilar neighbors' contributions are suppressed. Motived by multitask learning, an adaptive sparse representation is introduced to integrate different characteristics from the series of enhanced HSIs. The sparse coefficients of a given unknown pixel can be obtained from this representation and then used for classification. Experiments conducted on three benchmark data sets demonstrate that the proposed methodology leads to superior classification performance when compared to several well-known classifiers.

As a promising signal processing technique, a joint sparse model (JSM) has been used to integrate spatial and spectral information in the classification of remotely sensed images. This technique defines a local region of a fixed window size and assumes an equal contribution from each neighborhood pixel in the classification process of the test pixel. However, equal weighting is less reasonable for heterogeneous pixels, especially around class boundaries. Hence, a discontinuity preserving relaxation (DPR) method can be used to locally smooth the results without crossing the boundaries by detecting the discontinuities of an image in advance. In this letter, we developed a novel strategy that combines these two methods to improve the hyperspectral image classification. A JSM is first applied to obtain a posteriori probability distribution of pixels and then a DPR method is used to further improve the classification results. Experiments conducted on two benchmark data sets demonstrate that the proposed method leads to superior performance when compared with several popular algorithms.

In order to improve the predictive accuracy of human blood glucose quantitative analysis model with fourier transform infrared (FT-IR) spectroscopy, this paper uses a method named improved extended multiplicative scatter correction (Im-EMSC), which can effectively eliminate the scattering effects caused by human body strong scattering. The principal components of the differential spectra are used instead of the pure spectra of the analytes in this algorithm. Calibrate the unwanted physical characteristic through the shape of the curve of principal components, and extract the original glucose concentration information. Im-EMSC can efficiently remove most of the pathlength difference and baseline shift influences. Firstly, Im-EMSC is used as a preprocessing method, and then partial least squares (PLS) regression method is adopted to establish a quantitative analysis model. In this paper, the result of Im-EMSC is compared with those popular scattering correction algorithms of multiplicative scatter correction (MSC) and extended multiplicative scatter correction (EMSC) preprocessing methods. Experimental results show that the prediction accuracy has been greatly improved with Im-EMSC method, which is helpful for human noninvasive glucose concentration detection technology.

Hyperspectral imagery (HSI) is now in use for a wide range of applications such as land cover classification, climate change studies and environmental monitoring; however, the acquisition of HSI is still costly, and the curse of dimensionality i.e. the phenomenon that the amount of required training samples increases exponentially as the dimensionality increases linearly, makes it difficult to exploit the full potential of machine learning in HSI classification. To resolve the problem, we propose a novel framework for spatial-spectral HSI classification in this article. A soft support vector machine (SVM) and a probabilistic joint sparsity model (JSM) are proposed to compute a posteriori probabilities of the test pixels, respectively; and the probability scores are then fused by a linear opinion pool. Furthermore, a Markov random field (MRF) model is used as a maximum a posteriori (MAP) segmentation method for further regularization of the neighbor information to derive the labels for pixels. Extensive experiments conducted on three commonly-used benchmarking data sets show that the proposed probabilistic fusion method outperforms a number of well-known spatial-spectral HSI classification techniques.