Adaptive Haze Removal for Single Remote Sensing Image

Xie, Fengying; Chen, Jiajie; Pan, Xin; Jiang, Zhiguo

doi:10.1109/access.2018.2879893

Cited by 28 publications

(13 citation statements)

References 35 publications

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“…After the dark channel prior (DCP) algorithm was proposed by He [12], the dehazing effect has made great progress. On the basis of DCP, a series of remote sensing image defogging methods have been proposed [13,14]. The physical model-based remote sensing image defogging methods rely on the corresponding a priori knowledge [12,15] to obtain the scene depth information, which is uncertain and may affect the defogging performance.…”

Section: Traditional Remote-sensing Image Defogging Methodsmentioning

confidence: 99%

“…The estimation of atmospheric light was improved, and the estimation equation of the transfer function was re-derived according to the combination of both the transformed model and prior dark channels. Xie [14] proposed the dark channel saturation prior by analyzing the relationship between the dark channels and the saturation of the fog-free remote sensing image. According to the proposed dark channel saturation prior, the best transfer function was estimated and the haze imaging model is applied to defog.…”

Section: Traditional Remote-sensing Image Defogging Methodsmentioning

confidence: 99%

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Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Zhu

Luo

et al. 2021

Remote Sensing

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Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.

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Section: Traditional Remote-sensing Image Defogging Methodsmentioning

confidence: 99%

Section: Traditional Remote-sensing Image Defogging Methodsmentioning

confidence: 99%

Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Zhu

Luo

et al. 2021

Remote Sensing

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“…To validate the effectiveness of the haze removal method, two existing methods were selected for comparison, including the classical DCP method (CDCP) [21] and the dark channel-saturation prior method (DCSP) [84]. For a fair comparison, all parameters in these methods were set to be optimal via iterative adjustment.…”

Section: Comparisons Of Dehazing and Thin Cloud Removal Methodsmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

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Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

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“…Since the excellent performance of DCP in general picture dehazing, an increasing number of DCP-based approaches have been presented for haze removal in earth observation images [39]. In [40] and [41], different filters were employed to replace the soft matting to refine the coarse medium transmission.…”

Section: Introductionmentioning

confidence: 99%

A Semiphysical Approach of Haze Removal for Landsat Image

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.

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Adaptive Haze Removal for Single Remote Sensing Image

Cited by 28 publications

References 35 publications

Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

A Semiphysical Approach of Haze Removal for Landsat Image

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