scholarly journals Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain

2020 ◽  
Vol 13 (1) ◽  
pp. 101
Author(s):  
Noorbakhsh Amiri Golilarz ◽  
Hui Gao ◽  
Saied Pirasteh ◽  
Mohammad Yazdi ◽  
Junlin Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Gaussian Distribution ◽  
Noise Suppression ◽  
Hyperspectral Image ◽  
Optimization Algorithms ◽  
Threshold Value ◽  
Noise Removal ◽  
Remote Sensing Images ◽  
Generalized Gaussian Distribution ◽  
Non Linear

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [−σn, σn], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods.

scholarly journals Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

2021 ◽  
Author(s):  
P. L. Arun ◽  
R Mathusoothana S Kumar
Keyword(s):  
Remote Sensing ◽  
Conditional Probability ◽  
Traffic Control ◽  
Road Safety ◽  
Image Inpainting ◽  
Computational Time ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Occlusion Removal

AbstractOcclusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

scholarly journals Improved Piecewise Linear Transformation for Precise Warping of Very-High-Resolution Remote Sensing Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2235 ◽  
Author(s):  
Han ◽  
Kim ◽  
Yeom
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Linear Transformation ◽  
Affine Transformation ◽  
Piecewise Linear ◽  
Transformation Model ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Very High

A large number of evenly distributed conjugate points (CPs) in entirely overlapping regions of the images are required to achieve successful co-registration between very-high-resolution (VHR) remote sensing images. The CPs are then used to construct a non-linear transformation model that locally warps a sensed image to a reference image’s coordinates. Piecewise linear (PL) transformation is largely exploited for warping VHR images because of its superior performance as compared to the other methods. The PL transformation constructs triangular regions on a sensed image from the CPs by applying the Delaunay algorithm, after which the corresponding triangular regions in a reference image are constructed using the same CPs on the image. Each corresponding region in the sensed image is then locally warped to the regions of the reference image through an affine transformation estimated from the CPs on the triangle vertices. The warping performance of the PL transformation shows reliable results, particularly in regions inside the triangles, i.e., within the convex hulls. However, the regions outside the triangles, which are warped when the extrapolated boundary planes are extended using CPs located close to the regions, incur severe geometric distortion. In this study, we propose an effective approach that focuses on the improvement of the warping performance of the PL transformation over the external area of the triangles. Accordingly, the proposed improved piecewise linear (IPL) transformation uses additional pseudo-CPs intentionally extracted from positions on the boundary of the sensed image. The corresponding pseudo-CPs on the reference image are determined by estimating the affine transformation from CPs located close to the pseudo-CPs. The latter are simultaneously used with the former to construct the triangular regions, which are enlarged accordingly. Experiments on both simulated and real datasets, constructed from Worldview-3 and Kompsat-3A satellite images, were conducted to validate the effectiveness of the proposed IPL transformation. That transformation was shown to outperform the existing linear/non-linear transformation models such as an affine, third and fourth polynomials, local weighted mean, and PL. Moreover, we demonstrated that the IPL transformation improved the warping performance over the PL transformation outside the triangular regions by increasing the correlation coefficient values from 0.259 to 0.304, 0.603 to 0.657, and 0.180 to 0.338 in the first, second, and third real datasets, respectively.

Stripe Noise Removal from Remote Sensing Images

2021 ◽  
Author(s):  
P.Mohana Satya ◽  
Samudrala Jagadish ◽  
V. Satyanarayana ◽  
Mahesh K Singh
Keyword(s):  
Remote Sensing ◽  
Noise Removal ◽  
Remote Sensing Images

scholarly journals NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 593-598
Author(s):  
S. Priya ◽  
R. Ghosh ◽  
B. K. Bhattacharya
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Hyperspectral Data ◽  
Correlation Technique ◽  
Spectral Correlation ◽  
Spectral Angle Mapper ◽  
Non Linear

<p><strong>Abstract.</strong> Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands. The large number of continuous bands present in hyperspectral data introduces the problem of computational complexity due to presence of redundant information. There is a need for dimensionality reduction to enhance the ability of users for better characterization of features. Due to presence of high spectral correlation in the hyperspectral datasets, optimum de-correlation technique is required which transforms the hyperspectral data to lower dimensions without compromising with the desirable information present in the data. In this paper, focus has been to reduce the spectral dimensionality problem. So, this research aimed to develop computationally efficient non-linear autoencoder algorithm taking the advantage of non-linear properties of hyperspectral data. The proposed algorithm was applied on airborne hyperspectral image of Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) over Anand region of Gujarat and the performance of the algorithm was evaluated. The Signal-to-Noise Ratio (SNR) increased from 22.78 dB to 48.48 dB with increase in number of nodes in bottleneck layer for reconstruction of image. Spectral distortion was also measured using Spectral Angle Mapper Algorithm (SAM), which reduced from 0.38 to 0.05 with increase in number of nodes in bottleneck layer up to 10. So, this algorithm was able to give good reconstruction of original image from the nodes present in the bottleneck layer.</p>

scholarly journals EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 479-486
Author(s):  
H. Teffahi ◽  
N. Teffahi
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Classification ◽  
Mathematical Morphology ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Images ◽  
Spatial Classification

Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sensing images for thematic mapping purposes such as agricultural mapping, urban mapping, emergency mapping in case of natural disasters... In recent years, mathematical morphology and deep learning (DL) have been recognized as prominent feature extraction techniques that led to remarkable spectral-spatial classification performances. Among them, Extended Multi-Attribute Profiles (EMAP) and Dense Convolutional Neural Network (DCNN) are considered as robust and powerful approaches such as the work in this paper is based on these two techniques for the feature extraction stage and used in two combined manners and constructing the EMAP-DCNN frame. The experiments were conducted on two popular datasets: “Indian Pines” and “Huston” hyperspectral datasets. Experimental results demonstrate that the two proposed approaches of the EMAP-DCNN frame denoted EMAP-DCNN 1, EMAP-DCNN 2 provide competitive performances compared with some state-of-the-art spectral-spatial classification methods based on deep learning.

scholarly journals DE-striping hype spectral Remote Sensing Images using Deep Convolutional Neural Network

2021 ◽  
Vol 9 (4) ◽  
Author(s):  
Maad M. Mijwil ◽  
Atheel Sabih Shaker ◽  
Alaa Wagih Abdulqader
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Hyperspectral Image ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Evaluation Phase ◽  
Numerous Data ◽  
Hyperion Data ◽  
Hyperspectral Remote Sensing Images

Hyperspectral far off detecting records reflectance or emittance information in a huge amount of bordering and tight unearthly groups, and accordingly has numerous data in distinguishing and planning the mineral zones. Then again, the science and natural information gives us some other productive data about the actual qualities of pictures and channels that have been recorded from the surface. In this work, we focus on de-striping the hyperspectral remote sensing images on Hyperion data by applying Deep Convolutional Neural Network (DCNN). What is clear is the high significance of applying the sufficient pre-preparing on Hyperion information as a result of low sign to-commotion proportion. By contrasting the known layers of DCNN model for de-striping hyperspectral pictures. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of hyperspectral image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy 150s after the start of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.

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