In remote sensing semantic segmentation, imperfect labels are prevalent due to the complexities of data acquisition and annotation processes. Although recent approaches to noisy label correction in remote sensing segmentation have shown promising results, challenges remain in accuracy and generalizability, given the incomplete consideration of the complex mixing involved. Nearly all methods should address three key challenges to identify and correct noisy labels: when to select labels, which labels to select, and how to handle the selected labels. We propose a novel label correction framework, Adaptive Consistency-Guided Object-Level Label Correction with Mean Teacher (ACOC-MT), which effectively addresses all three of these challenges. The ACOC-MT framework determines when to conduct label correction through the Stable Early Learning Detection module, selects noisy labels using the Consistency Threshold Mask module, and corrects the labels through the Object Label Correction module. To validate against real-world noisy labels rather than simulated ones, we constructed three real-world noisy-labeled datasets, CPCTC-N, BBD250-N, and CFD-N, from three different remote sensing scenarios. Extensive experiments on these three datasets demonstrate the efficacy and superior performance of our approach.

Remote sensing visual grounding (RSVG) aims to accurately localize specific targets in remote sensing (RS) images based on natural language descriptions. However, existing RSVG datasets often contain overly simplistic textual descriptions, exhibit imbalanced object size distributions, and lack semantic connections between targets and the surrounding contexts. Moreover, current approaches rely on global visual features for region-level localization, while the interaction between visual and textual modalities remains limited. To address these challenges, we propose improvements from both the dataset and algorithm perspectives. First, we explore the semantic correlation between remote sensing objects and their scene context. Based on high-resolution Gaofen satellite imagery, we expand several typical object categories and construct richer textual descriptions that reflect object-background associations. By refining and extending the existing DIOR-RSVG dataset, we build a new dataset named DGF-RSVG. Second, to enhance the semantic alignment between global visual features and textual features, we propose a novel global vision-language multimodal feature interaction enhancement module (GME). In parallel, we design a local attention enhancement module (LAE) to facilitate fine-grained interaction between object-related textual features and regional visual proposals. These two modules form the foundation of our newly developed detection framework: the global-local attention enhanced detector (GLAED). Extensive experiments show that GLAED achieves state-of-the-art performance on the DGF-RSVG dataset, outperforming the closest competitor by 4.3% in [email protected]. It also achieves highly competitive results on the DIOR-RSVG dataset, demonstrating the effectiveness of both our proposed dataset and model framework.

With the increase of available spectral libraries, sparse unmixing has attracted great attention in the field of hyperspectral image unmixing. When the spatial information is integrated into the traditional sparse unmixing model, it achieves better performance. However, the less accurate description of the spatial structure limits the performance of the previous spatial sparse unmixing methods. To address this limitation, a new technique called superpixel based low-rank sparse unmixing (SpLRSU) is established, which encourages the local spatial consistency and the spatial continuity of the image. Specifically, superpixel segmentation is used to adaptively generate local homogeneous regions, and then the low-rank constraint is enforced on the abundance vectors of each spatial group to preserve the low-dimensional structure of superpixel blocks. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to promote the sparsity of fractional abundances in the spectral and spatial domains. The experimental results on the synthetic data set show that the newly proposed algorithm is superior to other advanced sparse unmixing algorithms.

Hyperspectral unmixing is a technique for selecting endmembers (pure spectral constituents) and their abundances (proportions). Recently, sparse unmixing is a semi-supervised method in which mixed pixels are represented in the form of combinations of a number of pure spectral signatures from a large spectral library. Compared with other methods, the sparse unmixing method exhibits significant advantages. However, most of these sparse unmixing methods were implemented in spatial domain, where the information is too scattered, redundant and susceptible to noise. In this paper, we propose a new unmixing method called spectral-spatial weighted sparse unmixing in the transform domain (SSTSU) to impose the abundance sparsity and enhance the anti-noise performance. The experimental results show that the proposed algorithm has better anti-noise performance and unmixing results compared with other advanced sparse unmixing methods.

The purpose of the paper is to implement an online fault diagnosis system for gas engine based on its data stream. The method proposed consists of three steps. First, we extract the features in a sliding window and reduce the dimension by PCA (Principal Components Analysis); second, the representative features are obtained by an AP (Affinity Propagation) clustering algorithm; finally, the fault pattern recognition is accomplished according to the distance between feature samples. The diagnosis results for three common faults show that this method achieves better performance than existing methods based on different clustering algorithms.

Signed social networks have become increasingly important in recent years because of the ability to model trust-based relationships in review sites like Slashdot, Epinions, and Wikipedia. As a result, many traditional network mining problems have been re-visited in the context of networks in which signs are associated with the links. Examples of such problems include community detection, link prediction, and low rank approximation. In this paper, we will examine the problem of ranking nodes in signed networks. In particular, we will design a ranking model, which has a clear physical interpretation in terms of the sign of the edges in the network. Specifically, we propose the Troll-Trust model that models the probability of trustworthiness of individual data sources as an interpretation for the underlying ranking values. We will show the advantages of this approach over a variety of baselines.

Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called Mpu, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our Mpu model consistently outperform other commonly-used baselines.