The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort-consuming to acquire. It is necessary to analyze SITS data with an unsupervised learning method. In this paper, we propose a new unsupervised learning method named Deep Temporal Iterative Clustering (DTIC) to deal with SITS data. The proposed method jointly learns a neural network’s parameters and the resulting features’ cluster assignments, which uses a standard clustering algorithm, K-means, to iteratively cluster the features produced by the feature extraction network and then uses the subsequent assignments as supervision to update the network’s weights. We apply DTIC to the unsupervised training of neural networks on both SITS datasets. Experimental results demonstrate that DTIC outperforms the state-of-the-art K-means clustering algorithm, which proves that the proposed approach successfully provides a novel idea for unsupervised training of SITS data.

The transient temperature response is of great importance for evaluating the thermal capacity of ground heat exchangers (GHE). Based on the composition line source theory and superposition principle, we have developed a novel analytical model in Laplace space for calculating the temperature transient response. In comparison to the existing models, this proposed model can account for the fluid thermal storage effect and heat rate difference between the two legs of the single U-tube. With the aid of this proposed model, we conduct a thorough sensitivity analysis to investigate the effects of different influencing factors on the thermal transient response. The calculated results show that fluid thermal storage and the rate difference can significantly influence the thermal response during the early studied period. Therefore, the effect of fluid thermal storage should not be neglected when the early-time thermal response is investigated. The thermal interference between the two legs will reduce the heat capacity of GHEs. A large distance between these two legs can be favorable for practical use.

Research on integrating statistical knowledge into deep learning models for earthquake forecasting has been limited. Traditional deep learning models require extensive parameter learning from scratch. This study proposes a Spatio-Temporal Convolutional (STC) model that incorporates spatio-temporal decay prior knowledge derived from the Epidemic-Type Aftershock Sequence (ETAS) into a convolutional kernel. This allows the STC model to have the prototype to learn the pattern of mainshocks to trigger aftershocks at the beginning of training, with only 4 neurons to fine-tune it. In California, the STC and the ETAS model are conducted for forecasting next-day earthquakes with magnitudes of M ≥3, M ≥4, and M ≥5. Both performances were assessed using the Receiver Operating Characteristic (ROC) curve, the Precision-Recall (PR) curve, and the Parimutuel Gambling Score (PGS). The evaluation results indicate that the STC model surpasses ETAS in forecasting next-day earthquakes not accidental. Furthermore, our analysis suggests that including earthquakes below the complete magnitudes can enhance the STC model’s classification performance, as small earthquakes also contain information about future earthquakes.

This study focused on improving the clustering performance of hyperspectral imaging (HSI) by employing the Generalized Orthogonal Matching Pursuit (GOMP) algorithm for feature extraction. Hyperspectral remote sensing imaging technology, which is crucial in various fields like environmental monitoring and agriculture, faces challenges due to its high dimensionality and complexity. Supervised learning methods require extensive data and computational resources, while clustering, an unsupervised method, offers a more efficient alternative. This research presents a novel approach using GOMP to enhance clustering performance in HSI. The GOMP algorithm iteratively selects multiple dictionary elements for sparse representation, which makes it well-suited for handling complex HSI data. The proposed method was tested on two publicly available HSI datasets and evaluated in comparison with other methods to demonstrate its effectiveness in enhancing clustering performance.

With the advancement of remote sensing satellite technology, the acquisition of Satellite Image Time-Series (SITS) data has significantly increased, providing new opportunities and challenges for land cover analysis. Traditional unsupervised clustering methods often struggle with the complexity of these data due to limitations in scalability and generalization capabilities. In response, this paper proposes a new unsupervised learning approach called deep temporal joint clustering (DTJC) designed for efficient pixel-wise clustering of SITS data. DTJC optimizes the reconstruction of temporal information along with clustering objectives, which not only preserves the temporal dynamics of the original data but also creates a feature space conducive to clustering. Experimental results show that DTJC achieves optimal clustering performance across four publicly available multi-spectral SITS datasets, including TimeSen2Crop, Cerrado Biome, Reunion Island, and Imperial datasets. Compared to traditional K-means and projection algorithms, DTJC significantly improves clustering accuracy, especially in environments with complex geographical distributions. Leveraging the principles of the K-means clustering algorithm, DTJC showcases remarkable performance improvements over traditional optimized K-means and projection algorithms in land cover analysis, heralding a new era in the unsupervised learning landscape of SITS data. The DTJC method greatly enhances the efficiency of SITS data analysis without the need for labeled data, making it a powerful tool for automated land cover classification and environmental monitoring.