Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Li, Liangliang; Ma, Hongbing

doi:10.3390/s21051756

Cited by 15 publications

(8 citation statements)

References 49 publications

(44 reference statements)

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“…After IHS color space transformation, components

and

of the MS image remain unchanged, and component

of the MS image with spatial details is used for histogram matching with the PAN image containing richer spatial details; the grayscale value of the PAN image is then calculated, and the histogram is equalized with the component

of the MS image, as expressed in Equation (8). According to the correspondence between

and

, the gray level of the PAN image is adjusted to obtain

with a higher degree of matching with the original image and

is matched with the PAN image histogram to obtain

[ 35 , 36 ].

where

denotes the PAN image grayscale value,

denotes the probability estimate, and

denotes the sum of

.…”

Section: Methodsmentioning

confidence: 99%

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Lu,

Zhou,

Huang

et al. 2024

Sensors

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Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

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“…After IHS color space transformation, components

and

of the MS image remain unchanged, and component

of the MS image, as expressed in Equation (8). According to the correspondence between

and

, the gray level of the PAN image is adjusted to obtain

with a higher degree of matching with the original image and

is matched with the PAN image histogram to obtain

[ 35 , 36 ].

where

denotes the PAN image grayscale value,

denotes the probability estimate, and

denotes the sum of

.…”

Section: Methodsmentioning

confidence: 99%

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Lu,

Zhou,

Huang

et al. 2024

Sensors

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show abstract

“…Liu et al [20] introduced a change detection method based on mathematical morphology and a k-means clustering model, and the accuracy of the change detection improved. The nonsubsampled contourlet transform (NSCT) and nonsubsampled shearlet transform (NSST) are widely used in image fusion and denoising [21][22][23][24][25][26][27]. Chen et al [28] introduced the NSCT-hidden Markov tree (NSCT-HMT) model to the remote sensing image change detection; Li et al [29] proposed a multitemporal remote sensing image change detection algorithm based on the NSCT denoising model.…”

Section: Sar Image Preprocessingmentioning

confidence: 99%

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Jia

2021

Remote Sensing

Self Cite

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Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

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“…It is necessary to improve the NSCT's multi-scale decomposition processing efficiency. Research has found that image blurring and poor contrast in various types of images can be significantly improved by the shearlet transform and the enhanced non-subsampled shearlet transform (NSST) [23,24]. In addition, the NSST achieves better contrast enhancement and clear target contours.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Zhou,

Chen,

Tang

et al. 2024

Remote Sensing

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Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

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Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Cited by 15 publications

References 49 publications

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

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