Soft sensor technology is widely used in the industries to handle highly nonlinear, dynamic, time-dependent sequence data of industrial processes for predicting the key variables associated with auxiliary process variables. Many existing soft sensor algorithms based on deep-learning are able to build complex nonlinear models but ignore the dynamic characteristics of processes. The long short-term memory (LSTM) neural network is exploited to solve the modeling issue which is related to strong time-varying features. In this paper, a novel multi-step sequence-to-sequence model based on attention LSTM (MA-LSTM) neural networks is proposed to improve the soft sensor modeling performance of industrial processes with strong dynamics and nonlinearity. The LSTM-based encoder-decoder architecture with the attention mechanism is applied to extract inherent characteristics relevant to the quality variables and capture both the long-term and short-term dependences of sequence data. Instead of the traditional sequence-to-point quality prediction architecture, the improved architecture is designed to predict multi-step quality variables, and the intermediate results are fed into an additional one-dimensional weighted convolution module to obtain accurate prediction results. The superiority of the proposed framework is demonstrated through a debutanizer column case and a sulfur recovery process.

In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result.

Although domain adaptation has been extensively studied in natural image-based segmentation tasks, the research on cross-domain segmentation for very-high-resolution (VHR) remote sensing images (RSIs) still remains underexplored. The VHR RSI-based cross-domain segmentation mainly faces two critical challenges: 1) large area land covers with many diverse object categories bring severe local patch-level data distribution deviations, thus yielding different adaptation difficulties for different local patches and 2) different VHR sensor types or dynamically changing modes cause the VHR images to go through intensive data distribution differences even for the same geographical location, resulting in different global feature-level domain gaps. To address these challenges, we propose a curriculum-style local-to-global cross-domain adaptation framework for the segmentation of VHR RSIs. The proposed curriculum-style adaptation performs the adaptation process in an easy-to-hard way according to the adaptation difficulties that can be obtained using an entropy-based score for each patch of the target domain and, thus, well aligns the local patches in a domain image. The proposed local-to-global adaptation performs the feature alignment process from the locally semantic to globally structural feature discrepancies and consists of a semantic-level domain classifier and an entropy-level domain classifier that can reduce the above cross-domain feature discrepancies. Extensive experiments have been conducted in various cross-domain scenarios, including geographic location variations and imaging mode variations, and the experimental results demonstrate that the proposed method can significantly boost the domain adaptability of segmentation networks for VHR RSIs.

Recently, the autoencoder (AE) has received significant attention in the hyperspectral anomaly detection task. However, all existing AE-based anomaly detectors operate under the linear mixing model, which cannot accurately model the nonlinear mixing phenomenon in practical hyperspectral images (HSIs). Moreover, these AE-based detectors rarely consider the spatial information between pixels, which is crucial to obtain accurate results of anomaly detection. To address the above issues, this paper proposes a transformer-based AE framework (TAEF) for nonlinear hyperspectral anomaly detection. Specifically, the proposed AE framework adopts the transformer as the encoder so that not only the local spatial information, but also the transitive global spatial information can be considered. And the extended multilinear mixing model (EMLM) is embedded into the decoder to accurately characterize the high-order nonlinear mixing phenomenon. By using this transformer-based AE framework, the background of HSIs can be reconstructed effectively. Moreover, a novel method for generating patches is proposed in this paper to support the transformer in the characterization of the transitive global spatial information. Besides, to further improve the accuracy of the background reconstruction, the local-clustering method is adopted to decrease the potential anomalies and increase the sparse backgrounds in the meantime. Finally, the anomalous level of pixel is calculated by the reconstruction error. The experimental results on various real hyperspectral datasets demonstrate that the proposed TAEF outperforms the current state-of-the-art anomaly detectors. In addition, our code is available at: https://github.com/I3ab/TAEF.

Traditional target detection (TD) algorithms for hyperspectral imagery (HSI) typically suffer from background interference. To alleviate this problem, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to separate the background and target apart. With the use of TPCA, HSI is decomposed into a principal component part and a residual part with the spatial-spectral information of the HSI being fully exploited, and TD is performed on the latter. Moreover, an effective distinction in scheme can be made between a HSI tensor’s spatial and spectral domains, which is in line with the physical meanings. Experimental results from both synthetic and real hyperspectral data show that the proposed method outperforms other preprocessing methods in improving the TD accuracies. Further, target detectors that combine the TPCA preprocessing approach with traditional target detection methods can achieve better results than those of state-of-the-art methods aiming at background suppression.