Despite the proven effectiveness of deep learning technology in pine tree disease segmentation, acquiring a large volume of labeled data remains challenging and inefficient. Few-shot segmentation (FSS) uses a small amount of labeled data to guide the segmentation of unknown categories, further evolving into one-shot segmentation (OSS), which utilizes a single labeled sample to perform segmentation under conditions of extreme data scarcity. However, these methods are mostly applicable to natural images with clear boundaries and have not yet been applied to segmenting pine tree disease in autonomous aerial vehicle (AAV) remote sensing images. For this reason, we have designed the OSS model C2Net for the first time, which includes two main modules: 1) a prior contour awareness module (PCAM) that first generates a query image prior mask with contour response and then uses an iterative feature refinement unit (FRU) to refine features and accurately delineate the segmentation boundaries of pine tree disease and 2) an interclass relationship module (ICRM), which studies the vegetation index features of the support and query images, constructing importance weights that reflect the differences between categories, solving the visual similarity issue. Our experiments on field-collected and publicly available datasets demonstrate that C2Net excels in challenging OSS tasks, showing its ability to generalize across different sensor domains and various disease categories. Especially, on the field acquisition dataset, using just a single labeled pine tree disease image achieves an intersection over union (IoU) of 55.24% and an$F_{1}$of 71.24%.

Hyperspectral unmixing (HU) can obtain subpixel-level ground object information, which is crucial for the fine advancement of imaging spectrum processing technology. Deep learning (DL) has been widely used in HU recently because of its ability to deeply mine complex relevant features in data. Existing DL unmixing methods usually operate only in the original spatial-spectral feature domain. However, due to noise, spectral variation, and other factors, it is difficult to fully mine effective features and easy to interfere with by only relying on the original domain. To get over these obstacles, we propose an innovative stationary wavelet convolutional network (SWC-Net) for HU. Stationary wavelet transform (SWT) is introduced in SWC-Net to extend the original feature domain to feature domains with different frequencies, which promotes the multiview extraction of information. What is more, a new generative self-supervised feature learning strategy based on wavelet perspective (GSFL-W) is proposed for SWC-Net. More robust features can be obtained by GSFL-W by introducing noisy perturbations into high-frequency inputs and forcing the network to generate the original inputs. The proposed SWC-Net surpasses the advanced approaches by sufficient experiments on one simulated and three real hyperspectral datasets. The code is publicly available athttps://github.com/UPCGIT/SWC-Net.

The wind speed forecasting is one of the key technologies in the operation of wind power plants and micro grids. However, the wind speed prediction is extremely complex and difficult due to the wind speed fluctuation and coupled weather conditions. This paper presents a multiperiod wind speed forecast approach based on data mining technique, which is the multi-objective optimal extreme learning machine with the association rules (AR-MOELM). On one hand, in order to improve the performance and efficiency of the extreme learning machine (ELM), the number of the hidden layer nodes and the penalty factor are optimized through multi-objective particle swarm algorithm (MOPSO)and membership function. On the other hand, the association rules(AR)are adopted to extract the preferable combination of the meteorological factors into the input matrix of the ELM for each season. The evaluation of the AR-MOELM is depicted in the Python environment by ultra-short-term, short-term and medium-term prediction experiments based on real wind speed datasets. The research results reveal that the proposed method improves the multiperiod wind speed prediction accuracy and outperforms existing methods.

Motivated by various applications in machine learning, the problem of minimizing a convex smooth loss function with trace norm regularization has received much attention lately. Currently, a popular method for solving such problem is the proximal gradient method (PGM), which is known to have a sublinear rate of convergence. In this paper, we show that for a large class of loss functions, the convergence rate of the PGM is in fact linear. Our result is established without any strong convexity assumption on the loss function. A key ingredient in our proof is a new Lipschitzian error bound for the aforementioned trace norm-regularized problem, which may be of independent interest.