Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery

Zheng, Yuan; Dai, Qinling; Tu, Zhigang; Wang, Leiguang

doi:10.3390/ijgi6120404

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…In Equation 7, the spatial details are determined by the difference between P and P l , which has a low spatial resolution. In most GF-based pansharpening algorithms, the output filter image Q k of the kth band is applied as the synthetic intensity image of the pansharpening process to inject the optimal spatial details of the panchromatic image into each multispectral band [36][37][38]. These methods are similar to the MRA method insomuch as they generate the optimal panchromatic image with a low spatial resolution.…”

Section: Gf-based Pansharpening Algorithmmentioning

confidence: 99%

“…This means that the optimal resized multispectral image for pansharpening can be generated using GF. When applying GF, the window size ω and regularization parameter ε are set to 2 and 0.1, respectively, in reference to the results of Zheng et al [37] related to pansharpening. In the proposed algorithm, pansharpening is performed by utilizing the filter output image obtained by GF as a multispectral image.…”

Section: Local Injection Gains Based On a Sigmoid Functionmentioning

confidence: 99%

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Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Choi¹,

Park²,

Seo³

2019

Remote Sensing

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Pansharpening algorithms are designed to enhance the spatial resolution of multispectral images using panchromatic images with high spatial resolutions. Panchromatic and multispectral images acquired from very high resolution (VHR) satellite sensors used as input data in the pansharpening process are characterized by spatial dissimilarities due to differences in their spectral/spatial characteristics and time lags between panchromatic and multispectral sensors. In this manuscript, a new pansharpening framework is proposed to improve the spatial clarity of VHR satellite imagery. This algorithm aims to remove the spatial dissimilarity between panchromatic and multispectral images using guided filtering (GF) and to generate the optimal local injection gains for pansharpening. First, we generate optimal multispectral images with spatial characteristics similar to those of panchromatic images using GF. Then, multiresolution analysis (MRA)-based pansharpening is applied using normalized difference vegetation index (NDVI)-based optimal injection gains and spatial details obtained through GF. The algorithm is applied to Korea multipurpose satellite (KOMPSAT)-3/3A satellite sensor data, and the experimental results show that the pansharpened images obtained with the proposed algorithm exhibit a superior spatial quality and preserve spectral information better than those based on existing algorithms.

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Section: Gf-based Pansharpening Algorithmmentioning

confidence: 99%

Section: Local Injection Gains Based On a Sigmoid Functionmentioning

confidence: 99%

Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Choi¹,

Park²,

Seo³

2019

Remote Sensing

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“…They use Fuzzy Inference Scheme (FIS) to perform image fusion of PAN and MS image [25]. Zheng et al (2017) uses guided image filtering to fuse GF2 (GaoFen-2) images. It is used for combining feature of two different source images such as PAN and MS image.…”

Section: Introductionmentioning

confidence: 99%

Fused LISS IV Image Classification using Deep Convolution Neural Networks

Maheswari¹,

Rajesh²

2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

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“…After one echo has been digitized into a one-dimensional signal, the image filtering algorithm can be used to suppress the noise of the LADAR signal. The most widely-used noise suppression methods in image processing are Gaussian algorithm [14], the bilateral filtering algorithm [15,16], and the guided filtering algorithm [17,18].…”

Section: Introductionmentioning

confidence: 99%

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

Xia

Chen

Wang

et al. 2019

Sensors

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The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters’ parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods.

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Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery

Cited by 13 publications

References 32 publications

Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Fused LISS IV Image Classification using Deep Convolution Neural Networks

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

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