Unsupervised spectral unmixing consists of representing each observed pixel as a combination of several pure materials known as endmembers, along with their corresponding abundance fractions. Beyond the linear assumption, various nonlinear unmixing models have been proposed, with the associated optimization problems solved either by traditional optimization algorithms or deep learning techniques. Current deep learning-based nonlinear unmixing mainly focuses on additive, bilinear-based formulations. The multilinear mixing model (MLM) offers a unique perspective by interpreting the reflection process by discrete Markov chains, allowing it to account for interactions between endmembers up to infinite order. However, explicitly simulating the physics of MLM using neural networks has remained a challenging problem. In this paper, we propose a novel autoencoder-based network for unsupervised unmixing based on MLM. Leveraging an elaborate network design, this approach explicitly models the relationships among all model parameters: endmembers, abundances, and transition probability. The network operates in two modes: MLM-1DAE, which considers only pixel-wise spectral information, and MLM-3DAE, which explores spectral-spatial correlations within input patches. Experiments on both the synthetic and real datasets validate the effectiveness of the proposed method, demonstrating competitive performance compared to classic MLM-based solutions. The code is available at https://github.com/ting-Fang09/Hyperspectral-unmixing-MLM-AE.

The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an artificial neural network (ANN) model supervised by centerloss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel convolutional neural network (CNN)-based spatial feature fusion (CSFF) algorithm, which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral-spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral-spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state of the art by declining 20%-50% classification failures in experiments on three well-known hyperspectral images.

Spectral unmixing of hyperspectral images is an important issue in the fields of remote sensing. Jointly exploring the spectral and spatial information embedded in the data is helpful to enhance the consistency between mixing/unmixing models and real scenarios. This paper proposes a graph regularized nonlinear unmixing method based on the recent multilinear mixing model (MLM). The MLM takes account of all orders of interactions between endmembers, and indicates the pixel-wise nonlinearity with a single probability parameter. By incorporating the Laplacian graph regularizers, the proposed method exploits the underlying manifold structure of the pixels’ spectra, in order to augment the estimations of both abundances and nonlinear probability parameters. Besides the spectrum-based regularizations, the sparsity of abundances is also incorporated for the proposed model. The resulting optimization problem is addressed by using the alternating direction method of multipliers (ADMM), yielding the so-called graph regularized MLM (G-MLM) algorithm. To implement the proposed method on large hypersepectral images in real world, we propose to utilize a superpixel construction approach before unmixing, and then apply G-MLM on each superpixel. The proposed methods achieve superior unmixing performances to state-of-the-art strategies in terms of both abundances and probability parameters, on both synthetic and real datasets.

We investigated the dimensionality of employer brand in the Chinese context and developed a scale to assess its underlying dimensions. We conducted 4 focus-group interviews to explore the domain content and components of employer brand. Based on the qualitative interviews, we conducted a survey (N = 912) to develop and validate the employer brand scale. The results revealed a 5-dimensional structure of employer brand in the Chinese context, which implied that explicit functional factors predominate the Chinese employer brand rather than the functional-symbolic structure of that prevails in the Western context. Among the 5 dimensions, work-life effectiveness and organization mark seemed unique to the Chinese context while the other 3 dimensions were similar to those found in the Western context. Testing of a 29-item scale representing the 5 dimensions showed good psychometric properties. This research contributes to the understanding of employer brand and how it is perceived by job applicants in a culture outside of the Western world.

To improve the classification performance in the context of hyperspectral image (HSI) processing, many works have been developed based on two common strategies, namely, the spatial-spectral information integration and the utilization of neural networks. However, both strategies typically require more training data than the classical algorithms, aggregating the shortage of labeled samples. In this letter, we propose a novel framework that organically combines the spectrum-based deep metric learning (DML) model and the conditional random field (CRF) algorithm. The DML model is supervised by the center loss to produce spectrum-based features that gather more tightly in Euclidean space within classes. The CRF with Gaussian edge potentials, which is first proposed for image segmentation tasks, is introduced to give the pixel-wise classification over the HSI by utilizing both the geographical distances between pixels and the Euclidean distances between the features produced by the DML model. The proposed framework is trained by spectral pixels at the DML stage and utilizes the half handcrafted spatial features at the CRF stage. This settlement alleviates the shortage of training data to some extent. Experiments on two real HSIs demonstrate the advantages of the proposed method in terms of both classification accuracy and computation cost.