17 accepted papers
Google Earth imagery, combined with building footprint databases, offers an efficient way to construct localized building datasets. However, the lack of orthorectification in these images leads to spatial misalignments between annotations and their corresponding roof locations. Adopting such misalig
Diffusion models serve as generative priors for dataset distillation, yet existing pipelines rely on per-sample update rules that evolve each synthetic image independently, limiting their ability to optimize collective set-level objectives. We propose Set-Coupled Guidance (SCG), a plug-and-play auxi…
Currently, the demand for higher video quality has grown significantly. However, satellite video has low resolution, complex motion, and weak textures. Haze interference further exacerbates the loss of motion information and texture details, hindering effective spatiotemporal feature fusion and fine…
In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate…
Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed…
Recently, improving the residual structure and designing efficient convolutions have become important branches of lightweight visual reconstruction model design. We have observed that the feature addition mode (FAM) in existing residual structure tends to lead to slow feature learning or stagnation…
The technique of semantic segmentation (SS) holds significant importance in the domain of remote sensing image (RSI) processing. The current research primarily encompasses two problems: 1) RSIs are easily affected by clouds and haze; 2) SS based on strong annotation requires vast human and time cost…
Due to the rich details of residential areas and the characteristics of remote sensing image sharpness vulnerable to haze, it will not only consume a lot of labor costs but also be very difficult to produce a large-scale dataset with strong labels. Therefore, the limited-sample dataset has become a…
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 nois…
As the annotation of remote sensing images requires domain expertise, it is difficult to construct a large-scale and accurate annotated dataset. Image-level annotation data learning has become a research hotspot. In addition, due to the difficulty in avoiding mislabeling, label noise cleaning is als…
Haze causes information loss and quality degradation in remote sensing images. Unsupervised learning-based dehazing methods aim to reduce reliance on paired hazy images and their labels. However, complex mapping relationships often increase the difficulty in network convergence, resulting in color d…
Due to the pixel-level accurate annotation of remote sensing images consumes a lot of labor costs, weak annotation semantic segmentation has become a hotspot in recent years. However, due to the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we propo…
Current UAV image haze removal methods often suffer from problems of insufficient dehazing and spectrum distortion, especially in regions with rich spectrum and texture information. In this paper, we propose a multi-dimensional saliency awareness unequal network to avoid texture loss and color disto…
Deep learning has shown great strength in regions of interest (ROIs) detection for remote sensing images (RSIs). However, for most of RSIs, the unbalanced distribution of positive and negative samples greatly limits the performance of the deep learning-based methods. To cope with this issue, we prop…
Synthetic Aperture Radar (SAR) imaging system plays an important role in earth observation research. This leads to the significance of target detection in SAR image. In this paper, we propose a novel saliency-driven oil tank detection method (SDD) for SAR images. First, we use the enhanced direction…
Transmission similarity is an important feature which can greatly increase the capability of convolutional neural network (CNN) to fit transmission map. However, it is not sufficiently utilized in existing algorithms. In this paper, we propose a novel light-weight similarity driven transmission comp…
Guided image filter is one of the most commonly used ways to refine transmission maps. However, since this filter transfers the structures of the guidance image to the filtering output, when the guidance image is the input image itself, even small textures in the input image will cause the change of…