Remote Sensing Image Enhancement Based on Wavelet Transformation

2012 ◽  
Vol 198-199 ◽  
pp. 223-226
Author(s):  
Ying Zhao ◽  
Ye Cai Guo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Theoretical Analysis ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Original Image ◽  
Remote Sensing Images ◽  
Enhancement Algorithm

The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.

Research of Image Compression Based on Fractal Coding

2012 ◽  
Vol 241-244 ◽  
pp. 418-422
Author(s):  
Dong Mei Wang ◽  
Jing Yi Lu
Keyword(s):  
Wavelet Transform ◽  
Theoretical Analysis ◽  
Image Compression ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Simulation Experiment ◽  
Low Frequency ◽  
Reconstructed Image ◽  
Wavelet Domain ◽  
Fractal Coding

The EZW and Fractal Coding were researched and simulated in this paper. And two drawbacks were discovered in these algorithm:the coding time is too long and the effect of reconstructed image is not ideal. Therefore, The paper studied the wavelet transformation in the fractal coding application, The wavelet coefficients of an image present two characteristics when the image is processed by wavelet transform: first characteristic is that the energy of an image is strongly concentrated in low frequency sub-image, second characteristic is that there is a similarity between the same direction in high frequency sub-images.but the fractal coding essence was precisely uses the similarity of wavelet transform image. The paper designed one kind of new Image Compression based on Fractal Coding in wavelet domain. The theoretical analysis and the simulation experiment indicated that, to some extent the method can reduce the coding time and reduce the MSE and enhance compression ratio of the reconstructed image and improve PSNR of the reconstructed image..

Remote sensing imaging simulation and cloud removal

2017 ◽  
Vol 31 (16-19) ◽  
pp. 1744079 ◽  
Author(s):  
Xifang Zhu ◽  
Feng Wu ◽  
Tao Wu ◽  
Chunyu Zhao
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Simulation Method ◽  
Complex Wavelet Transform ◽  
Remote Sensing Images ◽  
Low Level ◽  
Cloud Removal ◽  
High Level

Cloud obstacles obscure ground information frequently during remote sensing imaging which leads to valuable information losses. Removing clouds from a single image becomes challenging since no reference images containing cloud-free regions are available. In order to study cloud removal technologies and evaluate their performances, a method to simulate evenly and unevenly distributed clouds was proposed by analyzing the physical model of remote sensing imaging. Dual tree complex wavelet transform (DTCWT) and its features were introduced briefly. According to the frequency relationships between clouds and ground objects in remote sensing images, a novel cloud removal algorithm was proposed. The algorithm divided the cloud-contaminated image into low-level high frequency sub-bands, high-level high frequency sub-bands and low frequency sub-band by DTCWT. Low-level high frequency sub-bands were filtered to enhance the ground object information by Laplacian sharpening. The other two types of sub-bands were processed to remove clouds by cloud cover coefficient weighting (CCCW). The experiments were implemented to process cloud disturbed images produced by the proposed simulation method. The results of cloud removal from remote sensing images were analyzed. It proved the proposed algorithm is greatly superior to algorithms based on traditional wavelet transform and dark channel prior.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

DWT Based Blind Watermark in Relational Database

2013 ◽  
Vol 760-762 ◽  
pp. 2123-2128
Author(s):  
Yi Liu ◽  
Juan Wang
Keyword(s):  
Wavelet Transform ◽  
Theoretical Analysis ◽  
Data Fusion ◽  
Relational Database ◽  
High Frequency ◽  
Low Frequency ◽  
Blind Watermarking ◽  
Watermark Image ◽  
Data Set ◽  
Strong Robustness

Aiming to balance the robustness and imperceptibility of database watermark, propose a wavelet transform (DWT) based blind watermarking algorithm. The algorithm screens candidate attributes that can be embedded watermark and conducts subset segmentation and rearrangement, and then performs DWT transformation to the data subsets and the scrambled watermark image respectively. Embed the compressed low-frequency part of the watermark into the High-frequency part of the data set to achieve data fusion. Theoretical analysis and experiments show that the algorithm enjoys strong robustness and good invisibility.

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

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1756
Author(s):  
Liangliang Li ◽  
Hongbing Ma
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
High Frequency ◽  
Rapid Development ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Saliency Map ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Remote Sensing Image Fusion

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

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

2021 ◽  
Vol 13 (16) ◽  
pp. 3104
Author(s):  
Zhiqin Zhu ◽  
Yaqin Luo ◽  
Guanqiu Qi ◽  
Jun Meng ◽  
Yong Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Network Layer ◽  
Image Defogging ◽  
Frequency Components ◽  
Definition Of

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.

scholarly journals Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

2021 ◽  
Vol 13 (21) ◽  
pp. 4443
Author(s):  
Bo Jiang ◽  
Guanting Chen ◽  
Jinshuai Wang ◽  
Hang Ma ◽  
Lin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Learning Technology ◽  
Remote Sensing Images ◽  
Learning Methods ◽  
Atmospheric Scattering

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

scholarly journals Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

2021 ◽  
Vol 13 (9) ◽  
pp. 1858
Author(s):  
Xubin Feng ◽  
Wuxia Zhang ◽  
Xiuqin Su ◽  
Zhengpu Xu
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Spatial Quality

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

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