Search

Weakly annotated object segmentation for remote sensing images (RSIs) has attracted lots of attention due to its low labeling costs. However, annotating a huge amount of multiclass RSIs with image labels is still highly dependent on specific expert knowledge and, thus, requires considerable labeling costs. In this article, intending to further alleviate the labor-intensive labeling costs, we introduce a novel extremely weak annotation condition. In this condition, a substantial portion of samples are unlabeled, while merely a small number of samples are labeled with inexact image-level annotations. To achieve object segmentation under extremely weak annotations, we propose pseudo supervision relearning (PSRL), a novel three-stage framework with the core insight of effectively harnessing the potentially valuable supervision clues stored in abundant unlabeled data. In the first stage, the extremely weak annotations are switched to fine-grained but noisy pseudo supervision with the aid of image-level semantic learning and attention-guided data augmentation. Then, a category-aware dataset resplit strategy based on the masking perturbation mechanism is designed, aiming at adaptively selecting high-quality pixelwise pseudomasks from the artificially generated pseudo supervision and achieving class-balanced reliable-unreliable labels division. Ultimately, we devise a novel dynamic thresholding strategy (DTS)-guided relearning network to take full advantage of the valuable semantic information in the resplit dataset. Experimental results on two public RSI datasets show the effectiveness of the proposed framework. Utilizing less supervised information, the proposed method yields competing results compared to weakly supervised learning-based methods with complete image-level annotations.

This research unravels the strategic confluence of environmental leadership and cutting-edge Artificial Intelligence (AI) in the realm of Carbon Capture technology, and their combined effect on the financial fortitude of U.S. firms. It posits that a firm's environmental vision, when led by transformative green leadership, significantly propels the effective adoption of Carbon Capture solutions. Drawing on data from 145 publicly traded U.S. entities from the years 2017 to 2019, provided by the Carbon Disclosure Project and Compustat, this study meticulously explores the interrelation between a firm's environmental initiatives - including managerial focus, shared vision, proactive green strategies, and innovation—and its performance outcomes. The findings illuminate that while environmental commitments like management focus and a unified vision greatly encourage the embracement of Carbon Capture, the financial implications of these adoptions present a complex picture.

Recently, due to higher requirements for satellite video resolution, video super-resolution (VSR) has been extensively studied. However, the following problems have not been effectively resolved: 1) previous satellite VSR methods cannot achieve continuous-scale (integer and noninteger scale) VSR with a single model; 2) satellite video has complex ground and weak textures, which increases the difficulty of capturing motion information. In addition, existing methods adopt a unified alignment path, which leads to a drop in feature alignment accuracy; and 3) during feature fusion, previous methods ignore the correlation of spatiotemporal information in satellite video and cannot make full use of the spatiotemporal information. To address the above problems, in this article, we propose a novel network for continuous-scale satellite VSR (CSVSR). Specifically, first, for effective motion capture and accurate feature alignment, we design a residual-guided and time-aware dynamic routing alignment module, which can use feature residuals to lock motion areas and then dynamically select the corresponding alignment path based on the temporal distance. Second, we proposed a nonlocal mask-based feature fusion module (NMFFM) to exploit the correlation of the spatiotemporal features and complete effective spatiotemporal feature fusion. Third, to make our network adapt to multitask learning, we develop a scale-aware convolutional (SA-Conv) layer, which lets our network dynamically extract scale-adaptive features according to the input scale factors. Finally, we propose a continuous-scale upsampling module with a global feature implicit function (GFIF), which can achieve continuous-scale mapping from features to pixel values. In addition, we carefully design a novel training strategy to optimize our network. Comprehensive experiments verify that the proposed CSVSR has superior reconstruction performance on continuous-scale factors. The code will be available at https://github.com/chongningni/CSVSR.

Remote sensing images (RSIs) are often exposed to various degradation factors such as sensor noise and poor observation environments. These factors can result in the loss of image details, spectral distortion, and blurred scenes, which will severely hinder the performance of subsequent RSI applications. Unfortunately, current methods are only capable of addressing individual degradation factors, and lack the ability to handle multiple degradation factors within a unified framework. To mitigate this problem, we model the complex degradation process as a double-degradation model for RSIs, and propose a unified framework based on saliency-guided interaction learning (SGIL) for double-degradation RSI restoration. The proposed SGIL can simultaneously alleviate the influence of external environment degradation and internal sensor noise degradation, which comprises three parts: pseudo pixel supervision-based saliency analysis (PPS-SA), a task-aware interaction learning (TAIL) model, and a global feature enhancement module (GFEM). In PPS-SA, an explicit PPS-SA method is designed to generate saliency maps to effectively distinguish different texture complexities of RSIs, and a saliency-guided mapping selection mechanism is introduced to adapt to complex external environmental interference factor. In the TAIL model, a dehazing module and a super resolution (SR) module are specialized in alleviating the external environment interference and internal sensor noise, respectively. Instead of simple cascading, the two modules interact and collaborate with each other, which drastically improves the performance of double-degradation image restoration. To further improve the performance of the proposed SGIL, we also propose the GFEM to exploit global features and refine the restored results. Experiments on several RSI datasets demonstrate that the proposed SGIL achieves promising results on complex double-degradation RSIs.