It is essential to generate high quality class activation maps (CAMs) for accurate object segmentation of weakly annotated remote sensing images (RSIs). However, RSIs are highly susceptible to haze interference during capturing, adversely affecting the precision of object location. In addition, the diverse shapes and indistinguishable boundaries of targets in RSIs hinder the generation of ideal CAMs. To address these problems, we propose a region-aware weakly annotated object segmentation (RA-WAOS) model for hazy RSIs based on contrastive adversarial learning, where the haze condition of an image is regarded as its inherent style. The haze style projector (HSP) optimized by contrastive learning is specially designed to obtain haze style embeddings, and the adversarial training of HSP and RA-WAOS is adopted to narrow the gap between different styles in the latent space. Experimental results reveal that our proposal performs superiorly on hazy RSIs compared with other competing methods.

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.

Due to the different physical imaging models, most haze or rain removal methods for daytime images are not suitable for nighttime images. Fog effect produced by the accumulation of rain also brings great challenges to the restoration of low-light nighttime images. To deal well with the multiple noise interference in this complex situation, we propose a saliency-guided dynamic restoration network (SDRNet) that can remove rain and haze in nighttime scenes. First, a saliency-guided detail enhancement preprocessing method is designed to get images with clearer details as the auxiliary input. Second, following a rain removal network (RRN), we design an all-in-one nighttime dehazing network (ANDN) to estimate the spatially variable ambient light and transmission comprehensively by deforming the nighttime haze image model. Finally, an attention-based enhancement network (AEN) with dynamic fusion attention module is proposed to enhance the lowlight background image. Experimental results indicate that SDRNet can obtain clearer images with less fog and distortion compared with other methods.

Residential area (RA) segmentation is of great significance in the remote-sensing (RS) field. Training the segmentation network with image-level weakly annotated data (WAD) has become a research hot spot due to the easy access to classification labels. The quality of class activation maps (CAMs) is crucial for obtaining accurate segmentation results. Limited to irregular shapes, tortuous boundaries, and greatly varied scales of targets in RS images, generating high-quality CAMs is still a great challenge. To solve these problems, a novel weakly annotated RA segmentation model based on attention redistribution and co-learning (ARC) is proposed in this letter. We develop aggregate-and-distribute-based feature coupling (ADFC) to achieve the redistribution of attention on channel and spatial dimensions, which deals with multilevel features at the same time and makes them fully embedded together. Such an arrangement can effectively capture the shape characteristic of targets and filter out complicated backgrounds. To mitigate the impact of ambiguous regions like surroundings of boundaries and potential scattered houses, a confusion co-learning (CCL) strategy is designed to jointly explore the class-specific features and refine the cross-class features through a two-stream classifier with sharing weights, which helps generate sharper edges and discover ignored targets. Experimental results on GeoEye-1, SPOT5, and Landsat8 datasets reveal that our proposal outperforms the competing methods by a large margin in both subjective and objective assessments.

Bottom-up human pose estimation has raised more investigation in recent years, especially 2D keypoints regression. However, the state-of-art DEKR [1] still has some aspects (e.g., speed and accuracy) to be improved. In this paper, we propose a new framework named DEKRv2, which has been enhanced compared to DEKR. When DEKR calculates the offset of each keypoint, it only considers the features of the current keypoint and neglects the constraints between the adjacent keypoints. We adopt a coarse-to-fine feature extraction method to obtain a more accurate feature location of keypoints for this problem. We also find that the multibranch network in DEKR is very time-consuming because it is serial. We designed a more effective module based on Group Convolution to replace the multi-branches network in DEKR, and it can reduce reasoning time. Experiments on the CrowdPose dataset show that our method achieves superior compared with DEKR in speed or accuracy, respectively. In the single-scale test, our method obtains 66.6 mAP, 0.6 higher than DEKR. The codes and models are available at https://github.com/chaowentao/DEKRv2.

The demands for spectral and spatial quality in remote sensing (RS) images vary from region to region. Saliency detection is an effective tool to distinguish different regions with different demands. In this paper, we introduce saliency detection to satisfy these demands and propose a novel saliency-driven pan-sharpening network to further improve the fusion quality. Firstly, we combine foreground distribution with background prior to generate the initial saliency map, and implement least-square optimization to improve the detection accuracy. Then, we construct a dense spatial attention network trained through a new spatial-spectral-based loss function designed by saliency to meet diverse spectral and spatial needs of different regions. Thus, accurate fused images can be predicted. Experiments on SPOT-5 dataset indicate that our proposal has excellent properties with respect to the unified spatial-spectral quality against state-of-the-art methods.

Saliency detection is an essential tool to extract regions of interest (ROIs) in remote sensing (RS) images. However, many methods are applied to single image and cannot detect ROIs accurately due to the ignorance of high correlation among different RS images. Thus, we propose the Bayesian joint saliency detection method to extract ROIs. Firstly, we generate the prior saliency based on global color contrast according to co-clustering, which ensures that regions with similar features have the same saliency. Secondly, we produce the likelihood saliency by constructing intensity co-occurrence histogram, which can explore the intensity distribution of multiple images. Finally, due to the complex scenes in RS images, Bayesian enhancement strategy is applied to combine the prior saliency with the likelihood saliency, and obtain ROI with less background inference. Quantitative and qualitative experiments results indicate that our method outperforms competing methods and shows good performance in ROI extraction.

Due to the existence of speckle noise, background clutter, backscattering points, and geometric distortion of some targets in synthetic aperture radar (SAR) images, extracting multiscale and multilocation targets accurately is still a great challenge. To tackle these problems, a novel target detection method based on edge-aware and cross-coupling attention for SAR images is proposed in this letter. By enhancing the dependencies between targets in different locations, bridging the gap between different feature maps, and assisting the targets’ detection through cross-coupling with the edge-aware network, the performance of detecting multiscale targets in complex SAR images can be improved significantly. Specifically, residual spatial pyramid pooling (RSPP) and mixed pooling module (MPM)-based convolution block attention module (MCBAM) are combined in the decoding part to promote coupling between networks. Besides, the semi-dense connection is adopted in the encoding part based on residual convolution block (RCB), which can improve the ability of multiscale feature extraction and promote the acquirement of high-resolution features with strong semantic information. Experiments are conducted on the SAR oil tank dataset (OTD) and SAR residential area dataset (RAD). We compare our model with a traditional method and CNN-based algorithms. The experimental results verify that our model outperforms the competing models in both pixel level and geometric segmentation accuracy.

In remote sensing (RS) images, the demands for spectral and spatial quality of different regions are different, which means the unified fusion strategy on the whole image is not suitable for pan-sharpening task. Saliency, derived from visual attention mechanism, provides an effective way to satisfy these demands. Inspired by this, we propose a novel pan-sharpening method based on joint visual saliency analysis and parallel bidirectional network (JSPBN). Firstly, considering the complex scenes and uneven distribution of targets in RS images, we develop a Bayesian optimization based joint visual saliency analysis (B-JVSA) method that integrates prior saliency based on global color contrast with likelihood saliency based on joint co-occurrence histogram, which can highlight common salient regions while suppressing individual ones and irrelevant background by exploring the correlation among multiple RS images. Secondly, we construct a parallel bidirectional feature pyramid (PBFP) network to obtain coarse fusion features, fully considering individual characteristics of panchromatic images and multispectral images. Finally, we design a saliency-aware layer (SAL) according to B-JVSA to further refine the fusion effect in salient regions and non-salient regions. With the help of SAL, diverse strategies for certain regions are learned through two independent residual dense networks and thereby generating accurate fusion results. Experimental results show that our proposal performs better than the competing methods in both spatial quality enhancement and spectral fidelity preservation.

Various landscape characteristics and irregular object boundaries often make object extraction more difficult. Automated analysis of remote sensing (RS) images is challenging and saliency detection is an effective solution. Yet, many traditional algorithms emphasize simply on a single image and would, therefore, neglect the similarity of an image set. In this paper, concerning the relationships among images, a region of interest extraction model based on common features analysis for remote sensing images is proposed. Firstly, multi-image saliency maps, showing the common salient objects, are generated by clustering in RGB and CIELab color spaces. Next, a method, highlighting the salient region, is based on global and local saliency statistics analysis. Finally, regions of interest are segmented from original images according to saliency maps which have been made boundaries holding by superpixels. Experimental evaluation shows that compared with six existing models, we get more accurate saliency maps.

Extracting region of interest (ROI) plays an important role in many computer vision tasks. Recently, deep methods have shown excellent performance, however, when it comes to remote sensing image (RSI) domain, which lacks pixel-level annotations, training often leads to under-fitting and low-accuracy. In this paper, we propose a novel ROI extraction model based on unsupervised cross-domain adaptation for RSIs. Firstly, we pretrain the network, RS- RoINet, by large-scale natural datasets to learn general features. Through top-down propagation mechanism, we combine global and local information to generate the accurate edge of extraction maps. Then, we introduce domain adaptation module to reduce the difference between natural domain and RSI domain. Data from both domains is transferred into Reproducing Kernel Hilbert Space to measure the domain distribution distance. Finally, the model is adaptive for RSIs and extracts ROI more accurately. Compared with recent fully-supervised state-of-the-arts, our unsupervised method shows outstanding performance.

Requirements of spectral and spatial quality differ from region to region in remote sensing images, which is a significant challenge for pan-sharpening. Joint saliency analysis not only fulfills these demands, but also ensures the consistency by considering the mutual information of multiple images. Thus, we propose a pan-sharpening method based on joint saliency analysis and improved intensity- hue-saturation (IHS) for multiple remote sensing images. Firstly, we introduce an improved IHS method to obtain an accurate estimation of the intensity component. Then, we design a joint saliency analysis method based on global contrast calculation and intensity feature extraction, which is subsequently compensated by texture features to generate adaptive injection gains. Finally, we use the injection gains to inject the detail into the multispectral (MS) image. Experimental results demonstrate that our method has better performance in guaranteeing consistency in multiple images, improving spatial quality and preserving spectral fidelity.

Owing to the complicated background information and large data volume in remote sensing (RS) images, it's difficult to detect airport precisely and efficiently. In this paper, we propose a credible airport detection method based on saliency analysis and geometric feature detection. On the one hand, we use a novel saliency analysis model to measure both global contrast and spatial unity in RS images, by which the most salient region can be extracted accurately and the background can be suppressed preferably. On the other hand, considering the geometric features of the airport, a feature descriptor is conducted to detect proper hole structures and line segments in the saliency map. The experimental results indicate that our proposal outperforms existing saliency analysis models and shows good performance in the detection of the airport.

A novel practical quantum secret sharing protocol is proposed to share a private key between one and many parties based on four single-qubit states. The qubit capacity of this protocol is high due to the absence of bitwise exclusive OR of qubits' measuring results. It is also feasible with present-day technology, even when a great many participants are engaged, and secure against several common attacks. Besides, this protocol can be directly extended to deal with the many-to-many situation.

Quantum reversible logic circuits synthesis is one of the key technologies to construct quantum computer. The algebraic model for quantum information processing is a unitary matrix operator. Matrix can better reflect the quantum state evolution and the properties of quantum computation. Bidirectional matrix-based algorithm for quantum reversible logic circuits synthesis is proposed in this paper. The matrix representation of quantum reversible circuit and the circuit transformation rules of adjacent matrix are employed to construct any quantum reversible circuit in this paper. Compared with, the computational complexity of our algorithm has been decreased exponentially and the speed has been increased by about 105times. In addition, the types of the quantum reversible circuits synthesized by our algorithm are extended from only even permutations in to even and odd ones. we have synthesized 13!=6227020800 quantum reversible circuits, which can't be done by other algorithms.