Remote Sensing Image Fusion Research Articles

Моделирование помех от земной поверхности для определенной местности с помощью радиолокационных изображений и других данных дистанционного зондирования

Моделирование помех от земной поверхности для определенной местности с помощью радиолокационных изображений и других данных дистанционного зондирования

A New Technology of Remote Sensing Image Fusion

Wavelet packet transform stands out in the field of image fusion for its good frequency characteristics, and pulse coupled neural network (PCNN) has a unique advantage in image processing. To resolve the problem of multi-spectral remote sensing image fusion, in this paper, we put forward an algorithm combined the wavelet packet and PCNN based on HIS transform.The algorithm will be carried out as follows. Firstly, the TM images will be converted into HIS space, and then the luminance component and the high-resolution image will be broken into multi-scale by wavelet packet. Secondly, according to the frequency domain characteristics of the wavelet packet decomposition, we respectively use a method of weighted average in the low-frequency domain and a method of PCNN in the high frequency domain to select reconstruction coefficient.We can get a fused luminance component by taking inverse wavelet packet transform to be reconstructed. Finally, we can obtain the fusion image by taking inverse HIS transform. The experimental results show that the algorithm can be not only to retain the spectral information, but also greatly improve the spatial resolution of multispectral images, has a good fusion effect

An Improved Technology of Remote Sensing Image Fusion Based Waveled Packet and Pulse Coupled Neural Net

An Improved Technology of Remote Sensing Image Fusion Based Waveled Packet and Pulse Coupled Neural Net

An Improved Technology of Remote Sensing Image Fusion Based Waveled Packet and Pulse Coupled Neural Net

To resolve the problem of multi-spectral remote sensing image fusion ?in this paper, we put forward an algorithm based on the wavelet packet and pulse-coupled neural network (PCNN) of remote sensing image fusion .The algorithm will be carried out as follows. Firstly, the TM images will be converted into HIS space, and then the luminance component and the high-resolution image will be broken into multi-scale by wavelet packet. Secondly, according to the frequency domain characteristics of the wavelet packet decomposition, we respectively use a method of weighted average in the low-frequency domain and a method of PCNN in the high frequency domain to select reconstruction coefficient.We can get a fused luminance component by taking inverse wavelet packet transform to be reconstructed. Finally, we can obtain the fusion image by taking inverse HIS transform. The experimental results show that the algorithm can be better to retain the image edge and texture details. Keywords : image fusion; HSI; wavelet packet; pulse coupled neural networks DOI: http://dx.doi.org/10.11591/telkomnika.v10i3.617

Remote Sensing Image Fusion Based on Integer Wavelet Transformation and Ordered Nonnegative Independent Component Analysis

A novel remote sensing image fusion method is proposed based on integer wavelet transformation and ordered nonnegative independent component analysis (IWT-ONICA) to take advantage of the IWT characteristics of multi-resolution and high-speed computation and the fact that independent component analysis can remove higher-order redundancy. The proposed method is applied to the fusion of IKONOS satellite images. Statistical and visual results show that IWT-ONICA is superior to other fusion algorithms in terms of clarity and visual effect, while evaluation indicators such as the peak signal-to-noise ratio, correlation coefficient, spectral angle mapper, and relative dimensionless global error confirm its superior performance.

MODIS Image Fusion for Land Cover Classification

There are many spectral bands in MODIS images,as well as the spatial resolution is 250 m for channels 1 and 2,which make it possible for large area medium resolution land cover mapping.In order to utilize the spatial and spectral information effectively,taking band 1(b1) and/or band 2(b2) as the high resolution images and using the remote sensing image fusion methods of SFIM、HPF and PCA etc to fuse band 3~7(b3~b7).The results of different fusion were evaluated by spectral fidelity and classification accuracy.It was found that all the 6 fusion results can enhance the land cover classification accuracy to different degree;The results of b2 are better than b1;SFIM transform can improve classification accuracy to a large extent with relative smaller spectra distortion.Therefore the SFIM transform with b2 can be used to improve the spatial resolution and accuracy of MODIS large areas land cover classification.

Image composition by constraining responses of filters

This study proposes a unified method for a wide variety of image composition tasks. The proposed method is developed by constraining the responses of a set of filters to a target image. Each filter describes an attribute of the target image. Also, each attribute field is assumed to be equal to the corresponding attribute field of an input source. The constraints imposed by all those attributes are weighted heterogeneously and formulated into a minimisation problem. For different tasks, the required attributes (e.g. gradient, texture and colour constraints) can be specified by different sources (e.g. from a given image, constructed from several images or specified by users). The framework is flexible and can be configured to meet a variety of image editing tasks. To validate the effectiveness of this method, a variety of applications have been presented, including face data illumination removal, remote-sensing images fusion, texture transfer, multi-focus image fusion, seamless texture tiling and text layer transfer. The experimental results illustrate that the proposed method is effective in performance for the presented image editing tasks with comparisons to classical methods for the specified tasks.

Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition

Spectral distortion is one of the most significant problems in the field of remote sensing image fusion. In former studies, we found the fusion method based on independent component analysis (ICA) could solve this problem effectively, and attain a better balance between spectral and spatial information of fused image. However, this method may lead to spectral distort in a few local regions unavoidably. In this paper, an improved ICA fusion method is proposed. Improvement mainly includes two aspects. Firstly, a convenient way which uses negentropy to measure the nongaussianity of IC is presented to select main body independent component (MBIC); secondly, in order to avoid too much spatial information caused by replacing MBIC with panchromatic (PAN) image directly, a wavelet decomposition is applied to extract the detail information of PAN image. The results show that the proposed method can have a better trade-off between spectral and spatial information. Moreover, compared with ICA fusion method, it can not only improve the spatial resolution of fused image, but also eliminate the drawback of spectral distortion of ICA fusion method in some local regions.

基于Contourlet系数局部特征的选择性遥感图像融合算法

基于Contourlet系数局部特征的选择性遥感图像融合算法

Exploration of Remote Sensing Image Processing Method for Glacial Geomorphology Research

Taking Mt. Namjagbarwa region as an example, this paper explores a complete remote sensing image processing method for glacial geomorphology research. Based on the selection of Landsat7 ETM+ images, the remote sensing image processing method such as band selection, overlap, fusion, mosaic and so on is carried out. The result shows: ① right selection of remote sensing images and proper process based on the characteristics of research area and research purpose, not only reduce the process difficulty, but make a firm foundation for subsequent glacial geomorphology research; ②according to the computation of correlation coefficient between fusion images and original multi-spectral images, panchromatic high resolution images, the result shows that the principle component transformation method has better effect than IHS transformation method in remote sensing image fusion process.

Exploration of Remote Sensing Image Processing Method for Glacial Geomorphology Research

Taking Mt. Namjagbarwa region as an example, this paper explores a complete remote sensing image processing method for glacial geomorphology research. Based on the selection of Landsat7 ETM+ images, the remote sensing image processing method such as band selection, overlap, fusion, mosaic and so on is carried out. The result shows: ① right selection of remote sensing images and proper process based on the characteristics of research area and research purpose, not only reduce the process difficulty, but make a firm foundation for subsequent glacial geomorphology research; ②according to the computation of correlation coefficient between fusion images and original multi-spectral images, panchromatic high resolution images, the result shows that the principle component transformation method has better effect than IHS transformation method in remote sensing image fusion process.

Infrared and Visible Remote Sensing Imagery Fusion Based on Fuzzy Logic and NSCT

Although average or region energy method are widely used in calculating low-frequency coefficients in multi-sensor fusion, these methods still have shortcomings while fusing infrared and visible remote sensing imagery. Because of the differences of infrared and visible imaging mechanism, the extent of each source image’s contribution to the fused image is uncertain. Much information could be reduced and good result can not be obtained. Fuzzy logic is an efficient way to resolve uncertain problems. Therefore, fuzzy logic and nonsubsampled Contourlet transform (NSCT) are introduced into infrared and visible remote sensing image fusion and a novel multi-scale analysis method based on fuzzy logic is proposed in this paper. The spatial detail information of fused results is improved and the experiment indicates that the proposed method can get better visual quality.

Fusion of remote sensing images based on nonsubsampled contourlet transform and region segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear, accurate and comprehensive information than any single image. A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform (NSCT) and region segmentation. Firstly, the multispectral image is transformed to intensity-hue-saturation (IHS) system. Secondly, the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT. Then the NSCT coefficients of high and low frequency subbands are fused by different rules, respectively. For the high frequency subbands, the fusion rules are also unalike in the smooth and edge regions. The two regions are segregated in the panchromatic image, and the segmentation is based on particle swarm optimization. Finally, the fusion image can be obtained by performing inverse NSCT and inverse IHS transform. The experimental results are evaluated by both subjective and objective criteria. It is shown that the proposed method can obtain superior results to others.

A Remote Sensing Image Fusion Method Based on Wavelet Packet Transform

Remote sensing image fusion is becoming widely used in object recognition because of complementary nature of images from different sensors. The result of image fusion is a single image which is more suitable for human and machine perception or further image processing. In this paper, a new remote\|sensing image fusion method based on wavelet packet transform (WPT) to fusion a multi\|spectral image and a panchromatic image is studied. Firstly, the multi\|spectral image is transformed with PCA, and three principal components can be obtained. Secondly, the first principal component of the multi\|spectral image and panchromatic image are transformed with wavelet packet transform. Thirdly, their high\|frequency coefficients are combined efficiently, while the low\|frequency coefficients of the first principal component of the multi\|spectral image are preserved. Fourthly, the first principal component of the multi\|spectral image is transformed with inverse wavelet packet transform. Finally, the fusioned image is obtained by inverse PCAT. Visual result and statistical parameters indicate that the performance of our new method is efficient comparing with those of PCAT (principal component analysis Transform)\|based fusion method, IHST (Intensity\|Hue\|Saturation transform)\|based one, and WT (wavelet transform) \|based one. It not only enhances spatial detail information of the result image largely, but also preserves spectral information of the original multispectral image well.

Comment on Fusion Theory for Different Spatial and Spectral Remote Sensing Images

Multi\|source remote sensing images fusion is an important method to acquire a higher spatial and spectral image resolution of the same object and can also provide a better tool for its application. Some different fusion methods are reviewed in this paper, they are classified into pixel\|level, feature\|level and decision\|level three categories, all of which are explored in detail respectively and compared each other. Finally, future trends are put forward for discussion.

An image fusion method based on object-oriented classification

In image fusion of different spatial resolution multispectral (MS) and panchromatic (PAN) images, a spectrally mixed MS pixel superimposes multiple mixed PAN pixels and multiple pure PAN pixels. This verifies that with increased spatial resolution in imaging, a low spatial resolution spectrally mixed subpixel may be unmixed to be a pure pixel. However, spectral unmixing of mixed MS subpixels is rarely considered in current remote-sensing image fusion methods, resulting in blurred fused images. In the image fusion method proposed in this article, such spectral unmixing is realized. In this method, the MS and PAN images are jointly segmented into image objects, image objects are classified to obtain a classification map of the PAN image and each MS subpixel is fused to be a pixel matching the class of the corresponding PAN pixel. Tested on spatially degraded IKONOS MS and PAN images with a significant spatial resolution ratio of 8:1, the fusion method offered fused images with high spectral quality and deblurred visualization.

Contourlet hidden Markov Tree and clarity-saliency driven PCNN based remote sensing images fusion

In this paper a new Contourlet hidden Markov Tree (CHMT) and clarity-saliency driven Pulse Coupled Neural Network (PCNN) based fusion approach is proposed for remote sensing images fusion. Considering the failure of wavelet in representing the geometry of image edges and contours, we firstly use Contourlet to provide an efficient and flexible multiscale, multidirectional and anisotropy representation of remote sensing images, and then use CHMT model to describe the statistics of Contourlet coefficients, by taking the dependencies across scales, locations and directions into account. Because CHMT can span several adjacent directional subbands in the finer scales, which has similar statistical characteristic to scale, CHMT model can give more accurate description of images and has lower computational complexity than wavelet hidden Markov Tree (WHMT). Training the Contourlet coefficients of registered multisource images using Expectation Maximum (EM) algorithm, one can obtain the model parameters which is used to update the Contourlet coefficients. Low-frequency subbands are fused by the magnitude maximum rule. For the fusion of high-frequency directional subband, a PCNN is constructed based on the phenomena of synchronous pulse bursts in the animal visual cortexes, and the linking strength of each neuron in PCNN is determined by a new clarity-saliency measure of the subband images. New fire mapping images are obtained for each high-frequency subband taking part in the fusion. Some experiments are taken on some remote sensing images came from the USA Airborne Multisensor Pod System (AMPS) program, and the results show the superiorities of our proposed method to WHMT and Contourlets, both in subjective evaluation, implementation speed and some numerical guidelines.

A Remote Sensing Image Fusion Method based on Curvelet Transform and IHS Transform

Based on the features of the different mathematical models during the image processing,we proposed a remote sensing image fusion method,which is based on Curvelet transform and IHS transform.Firstly,the IHS transform is applied to multi-spectral image;then,the I component and the high-resolution image are fused through Curvelet transform;finally,we can obtain the result by inverse IHS transform.The experimental results show that the method has outstanding advantages,which maintain the spectral characteristics of the source image,and its results can be applied to the field of spectral analysis.

Data Assimilation Algorithm of Multi-fountain Remote Sensing Image Fusion

To solve the problem that the fusion rules could not be adjusted self-adaptively with the available fusion methods according to the following processing requirements of remote sensing (RS) image,this paper builds a data assimilation system by integrating the merit of model operator and observation operator,and presents an RS image fusion scheme based on data assimilation and genetic particle swarm algorithm.The arithmetic takes the plain-move invariant wavelet transform as a model operator,and takes the contrast pyramid as observation operator.Also the scheme ascertains the weights of the attribute indices according to their dependent degrees to attribute indices of image by following processing.And then the algorithm designs the objective function by using the weighted sum of evaluation indices.Moreover,the arithmetic optimizes the objective function by employing genetic particle swarm algorithm to obtain a higher definition RS image.This paper proves the superiority of the proposed scheme by syncretizing RS panchromatic image and multi-spectral image as well as inosculating infrared image and visible light image.

Adaptive remote sensing image fusion under the framework of data assimilation

The existing fusion methods cannot adjust fused images, adaptively according to the requirements of follow-up image processing steps, as well as the merits of different fusion methods, and are not easy to be integrated. Since a data assimilation system can integrate the advantage of a model operator and observer operator, a fusion framework based on the data assimilation concept is proposed, which can adaptively fuse different remote sensing images. Under this framework, a fusion method based on an independent component analysis and àtrous wavelet transform is used as a model operator, and another fusion method based on nonsubsampled contourlet transform is used as an observation operator. Meanwhile, image quantitative evaluation indicators are used as an objective function. Then, the genetic particle swarm algorithm is employed to optimize the objective function in order to gain a more suitable image. Finally, three sets of panchromatic images and multispectral images are used in experiments. The results show that the proposed algorithm can adjust fusion results adaptively, according to a particular objective function.