Hybrid High-Resolution Learning for Single Remote Sensing Satellite Image Dehazing

Chen, Xiang; Li, Yufeng; Dai, Longgang; Kong, Caihua

doi:10.1109/lgrs.2021.3072917

Cited by 30 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geological thinking is commonly proposed by researchers in the field of geology [16][17][20][21], which makes full use of the existing knowledge and relevant data in the field of geology and simulates the way of thinking of geological experts in interpretation [22][23]. An early representative study was an expert intelligent interpretation system based on high-resolution remote sensing images [24][25].…”

Section: Remote Sensing Data Information Extractionmentioning

confidence: 99%

Research on Remote Sensing Retrieval Method of PM2.5 Based on FY-4A Satellite with Multiple Machine Learning Methods

Li¹,

Liu²

2023

AJST

View full text Add to dashboard Cite

Satellite remote sensing technology can see the breadth and fineness that humans cannot see, and can see the spatio-temporal changes that humans cannot see. The inversion of PM2.5 based on remote sensing satellite has wide spatial coverage and relatively high spatial and temporal resolution, and the estimation results are reliable and easy to obtain, which can provide the data basis and scientific basis for managing PM2.5 pollution. This paper summarizes 2 aspects from remote sensing information processing technology and PM2.5 concentration estimation model. The development trend of current satellite remote sensing technology is analyzed from the perspective of the principle and development of satellite remote sensing technology, and the basic analytical thinking and analytical steps of current remote sensing information technology processing are described from the starting point of remote sensing information acquisition and processing technology; the advantages, disadvantages and adaptability of different models are pointed out through the comparative analysis of domestic and foreign estimation models of PM2.5 concentration. The study shows that the number of remote sensing satellites in China is increasing year by year, and remote sensing data and estimation models are being used effectively and improved gradually.

show abstract

Section: Remote Sensing Data Information Extractionmentioning

confidence: 99%

Research on Remote Sensing Retrieval Method of PM2.5 Based on FY-4A Satellite with Multiple Machine Learning Methods

Li¹,

Liu²

2023

AJST

View full text Add to dashboard Cite

show abstract

“…Chen and Huang 22 proposed a memory-oriented GAN (MO-GAN), which tries to extract the desired hazy features in an unpaired learning method toward single remote sensing image dehazing. H2RL-Net 23 is first attempted to explore an end-to-end hybrid high-resolution learning network framework termed to remove a single satellite image haze. Different from these methods, our goal is to inherit the strengths of CNN and transformer backbones via novel designs.…”

Section: Related Workmentioning

confidence: 99%

Learning an ensemble dehazing network for visible remote sensing images

Li,

Lu,

Fan

et al. 2023

J. Appl. Rem. Sens.

Self Cite

View full text Add to dashboard Cite

Image dehazing is an important preprocessing task since haze extremely degrades the image quality and hampers the application of remote sensing vision system. Although the deep learning-based method has been successful in image dehazing, there has been little effort to harmonize convolutional neural networks and transformer to better satisfy removing haze. In particular, local and global representation learning are equally important for the challenging image dehazing task. To this end, we propose an effective ensemble dehazing network (EDHN) for visible remote sensing images. Specifically, we introduce two key backbone modules for the developed ensemble framework, including operation-wise attention module and transformer module. The operation-wise attention module is designed for restoring spatially varying degradation, and the transformer module is employed to refine haze-free background textures and structures. Furthermore, residue channel prior and feature aggregation block are also incorporated into our ensemble architecture to further guide image reconstruction and boost image restoration. Experimental results show the superiority of our proposed EDHN and demonstrate the favorable performance against recent dehazing approaches.

show abstract

“…Liu et al [24] presented a novel trainable Grid-Dehaze Network (GDN) that indicates how confident the network is about the multi-scale features are learned. Later, these models succussed inspired great efforts invested in the development of aerial image dehazing methods, such as MRCNN [32], H2RL-Net [31], RSDehazeNet [16], where more effective network architectures were mainly designed. Unlike CNN, the GAN based methods have adopted generation and discrimination networks to regularize the dehazed image to have reliable colors and structures.…”

Section: A Aerial Image Dehazingmentioning

confidence: 99%

“…We use two synthetic datasets including the RESIDE dataset [20] and SateHaze1k dataset [29] with different haze distribution states. Besides, real-world datasets are collected to evaluate the performance of dehazing and two datasets are involved: Overwater dataset [47] and RealHaze dataset [31]. The detailed descriptions are tabulated in Table 3, including the synthetic and real-world datasets.…”

Section: A Datasets Setupmentioning

confidence: 99%

“…datasets have been established, such as SateHaze1k (three levels of fog, namely thin, moderate, and thick fog) [29], UN-HAZE (uniform haze-clear image pairs) [30], and NONUN-HAZE (nonuniform haze-clear image pairs) [30]. On the other hand, the recent aerial image dehazing algorithms exhibit remarkable performance, such as MRCNN [32], H2RL-Net [31], and RSDehazeNet [16]. However, it is worth noting that the above mentioned dehazing networks rely on paired synthetic data to train their models in a fully supervised manner, which directly learn a suitable representation from one-to-one mapping relationship, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN

Xie

2022

IEEE Access

View full text Add to dashboard Cite

Aerial image dehazing is an important preprocessing step, since haze extremely degrades the imaging quality and affects subsequent the applications of aerial imagery. Most current haze removal methods achieve encouraging performance by relying on paired synthetic data, while are limited to their generality and scalability in the practical tasks. To this end, this paper aims to learn an effective unsupervised dehazing model from an unpaired set of clear and hazy aerial images. Motivated by the great advantages of contrastive learning in unsupervised representation field, we first attempt to formulate a Asymmetric Contrastive CycleGAN dehazing framework (namely ACC-GAN) to maximize the mutual information between the hazy domain and the haze-free domain. In the latent representation space, the introduced contrastive constraint ensures that the restored image is pulled closer to the clear image and pushed away from the hazy image, so as to indirectly regularize the unsupervised dehazing process. Importantly, different from the standard CycleGAN, we develop an additional feature transfer network into the forward path to form the asymmetric structure of ACC-GAN, which can enhance encoded features from hazy domain to haze-free domain. During training, multi-dimension loss terms are jointly built into a loss committee for generating dehazed results with higher naturalness and better fidelity. Experimental results on synthesis and real-world datasets indicate that our method is superior to existing unsupervised dehazing approaches, and is also very competitive to other related supervised models.INDEX TERMS aerial imagery; haze removal; asymmetric CycleGAN; unsupervised learning; contrastive learning.

show abstract

Hybrid High-Resolution Learning for Single Remote Sensing Satellite Image Dehazing

Cited by 30 publications

References 18 publications

Research on Remote Sensing Retrieval Method of PM2.5 Based on FY-4A Satellite with Multiple Machine Learning Methods

Research on Remote Sensing Retrieval Method of PM2.5 Based on FY-4A Satellite with Multiple Machine Learning Methods

Learning an ensemble dehazing network for visible remote sensing images

Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN

Contact Info

Product

Resources

About