Original Multispectral Image Research Articles

A New Pansharpening Algorithm Based on Total Variation

In this letter, we present a new method for the pansharpening of multispectral satellite imagery. Pansharpening is the process of synthesizing a high spatial resolution multispectral image from a low spatial resolution multispectral image and a high-resolution panchromatic (PAN) image. The method uses total variation to regularize an ill-posed problem dictated by a widely used explicit image formation model. This model is based on the assumptions that a linear combination of the bands of the pansharpened image gives the PAN image and that a decimation of the pansharpened image gives the original multispectral image. Experimental results are based on two real datasets and the quantitative quality of the pansharpened images is evaluated using a number of spatial and spectral metrics, some of which have been recently proposed and do not need a reference image. The proposed method compares favorably to other well-known methods for pansharpening and produces images of excellent spatial and spectral quality.

基于局部相关性的遥感图像全色锐化算法

In order to enhance the spatial resolution of multispectral images and avoid severe spectral distortion,the amplified multispectral images and the original panchromatic image were decomposed by non-subsampled contourlet transform with the same layers.A 5×5sliding window around the fusion pixel of the matrix in the low frequency sub-band of the decomposed images was selected to compute the local correlation coefficient and fourth-order correlation coefficient.If the calculated local correlation coefficient was greater than the value of fourth-order correlation coefficient,the center pixel of the matrix of the high frequency coefficients of multispectral images was replaced by high frequency coefficients of panchromatic image because the position of the ground objects were similar on the spectral characteristics.Otherwise,the high frequency coefficients of multispectral image were kept unchanged. At last,the fused image was reconstructed from the joint of approximation coefficients of multispectral image and detail coefficients obtained from the fusion procedure with inverse non-subsampled contourlet transform.The experimental simulation was conducted on Landsat 7remote sensing images and the subjective and objective evaluation index of the fused results were given by comparing with the existing similar latest literature.The experimental results indicate that the proposed approach has good effects both on enhancing the original multispectral image and maintaining good spectral characteristics.

Spatial Quality Assessment of Pan-Sharpened High Resolution Satellite Imagery Based on an Automatically Estimated Edge Based Metric

Most of the existing pan-sharpening quality assessment methods consider only the spectral quality and there are just few investigations, which concentrate on spatial characteristics. Spatial quality of pan-sharpened images is vital in elaborating the capability of object extraction, identification, or reconstruction, especially regarding man-made objects and their application for large scale mapping in urban areas. This paper presents an Edge based image Fusion Metric (EFM) for spatial quality evaluation of pan-sharpening in high resolution satellite imagery. Considering Modulation Transfer Function (MTF) as a precise measurement of edge response, MTFs of pan-sharpened images are assessed and compared to those obtained from the original multispectral or panchromatic images. Spatial quality assessment of pan-sharpening is done by comparison of MTF curves of the pan-sharpened and reference images. The capability of the proposed method is evaluated by quality assessment of two different residential and industrial urban areas of WorldView-2 pan-sharpened images. Obtained results clearly show the wide spatial discrepancy in quality of Pan-sharpened images, resulting from different fusion methods, and confirm the need for spatial quality assessment of fused products. The results also prove the capability of the proposed EFM as a powerful tool for evaluation and comparison of different image fusion techniques and products.

Fusion of terrasar-x and rapideye data: a quality analysis

Abstract. This research compares and evaluates image fusion algorithms to achieve spatially improved images while preserving the spectral information. In order to compare the performance of fusion techniques both active and passive images were used. As an active image a high resolution, X-band, VV polarized TerraSAR-X data and as a multispectral image RapidEye data were used. RapidEye provides five optical bands in the 400–850 nm range and it is the first space-borne sensor which operationally gathers the red edge spectrum (690–730 nm) besides the standard channels of multi-spectral satellite sensors. The selected study area is in the low lands of Menemen (Izmir) Plain on the west of Gediz Basin covering both agricultural fields and residential areas. For the quality analysis, Adjustable SAR-MS Fusion (ASMF), Ehlers fusion and High Pass Filtering (HPF) approaches were investigated. In this study preliminary results of selected image fusion methods were given. The quality of the fused images was assessed with qualitative and quantitative analyses. For the qualitative analysis visual comparison was applied using different band combinations of fused image and original multispectral Rapid-Eye image. In the merged images color distortions regarding to SAR-optical synergy were investigated. Statistical analysis was carried out as quantitative analyses. In this respect Correlation Coefficient (CC), Standard Deviation Difference (SDD), Universal Image Quality Index (UIQI) and Root Mean Square Error (RMSE) were performed for quality assessments. In general HPF was performed best while ASMF was performed the worst in all results.

A comparative study of various pixel-based image fusion techniques as applied to an urban environment

In this paper, the use of high-resolution images for identification of urban features through pixel-based image fusion techniques is discussed. Fusion techniques are used to merge high spatial resolution panchromatic (PAN) image with low spatial resolution multispectral (MS) image to enhance the visual quality/appearance of some of the urban features present in the image. In this paper nine pixel-based fusion techniques, viz., Principal Component Analysis (PCA), multiplicative, Brovey transformation, wavelet analysis, subtractive, HPF, modified IHS, Ehlers, and hyperspherical color space, are discussed for the fusion of PAN and MS IKONOS images. The fused images are interpreted on the basis of visual comparison, correlation coefficients and histogram statistics. All the nine fusion techniques improved the resolution and the visual appearance of the original MS image. These are comparable to the resolution of the original PAN image. All the three resampling methods (nearest neighborhood, bilinear interpolation and the cubic convolution) do not have any significant effect on the final visual appearance of the fused images. All fusion techniques result in a change of the statistical parameters of the original images. The multiplicative technique results in major changes of the statistical parameters than the other techniques. After doing visual comparison and analyzing the statistical parameters, the Brovey and the PCA techniques seem to gather more information of urban features in fused PAN and MS IKONOS images.

Remote Sensing Image Fusion of Worldview-2 Satellite Data

In view of the situation that most image fusion methods make spectral distortion more or less; this paper proposes a spatial projection method by introducing the Gaussian scale space theory. According to mechanism of the human visual system represented by the Gaussian scale space, the spatial details feature information are extracted from the original panchromatic (PAN) and multispectral (MS) images, then the feature differences are projected into the original MS image to obtain the fused image. The experimental results with WorldView-2 images show that the proposed method can improve the spatial resolution of the fused MS image effectively, while it can make little spectral distortion to the fused images so as to maintain the great majority spectral information.

Comparative analysis of mapping burned areas from landsat TM images

Remote sensing is a major source of mapping the burned area caused by forest fire. The focus in this application is to map a single class of interest, i.e. burned area. In this study, three different data combinations were classified using different classifiers and quantitatively compared. The adopted classifiers are Support Vector Data Description (SVDD), a one-class classifier, Binary classifier Support Vector Machines (SVMs) and traditional Maximum Likelihood classifier (ML). At first, the Principal Component Analysis (PCA) was applied to extract the best possible features form the original multispectral image (OMI) and calculated spectral indices (SI). Then the resulting subset of features was applied to the classifiers. The comparative study has undertaken to find firstly, the best possible set of features (data combination) and secondly, an effective classifier to map the burned areas. The best possible set of features was attained by data combination- II (i.e., OMI information). Furthermore, the results of the SVM showed the high classification accuracies than ML. Experimental results demonstrate that even though the SVDD for mapping the burned areas doesn't showed the higher classification accuracy than SVM, but it shows the suitability for the cases with few or poorly represented labelled samples available. The parameters should be further optimized through the use of intelligent training for improving the accuracy of SVDD.

Pan-sharpening method via ARSIS concept under image super-resolution scheme

We present a novel fusion method to improve the spatial resolution of multispectral (MS) images, where the fused spectral images integrate the spectral information and spatial details from the original MS images and panchromatic (PAN) image, respectively. Band by band, a spectral image with high resolution is reconstructed from the original spectral image to take advantage of super-resolution technology. The pan-sharpening method via Amelioration de la Reso- lution Spatiale par Injection de Structures concept is further applied to obtain the fused images from the reconstructed spectral images and PAN image. Performance of the proposed method has been evalu- ated on the public optical satellite QuickBird images. Experimental results show that the fused spectral images both preserve the spatial details of high-resolution from the PAN image and have higher spec- tral resolution than the original spectral images. © 2012 SPIE and IS&T. (DOI: 10.1117/1.JEI.21.3.030501)

고해상도 SAR와 광학영상의 고주파 정보를 이용한 다중센서 융합

Synthetic Aperture Radar(SAR) imaging system is independent of solar illumination and weather conditions; however, SAR image is difficult to interpret as compared with optical images. It has been increased interest in multi-sensor fusion technique which can improve the interpretability of images by fusing the spectral information from multispectral(MS) image. In this paper, a multi-sensor fusion method based on high-frequency extraction process using Fast Fourier Transform(FFT) and outlier elimination process is proposed, which maintain the spectral content of the original MS image while retaining the spatial detail of the high-resolution SAR image. We used TerraSAR-X which is constructed on the same X-band SAR system as KOMPSAT-5 and KOMPSAT-2 MS image as the test data set to evaluate the proposed method. In order to evaluate the efficiency of the proposed method, the fusion result was compared visually and quantitatively with the result obtained using existing fusion algorithms. The evaluation results showed that the proposed image fusion method achieved successful results in the fusion of SAR and MS image compared with the existing fusion algorithms.

Image misalignment caused by decimation in image fusion evaluation

In the evaluation of image fusion methods, spatially degraded multispectral (MS) and panchromatic (PAN) images are frequently employed as test data sets. The degradation is implemented using either averaging or a combination of low-pass filtering and decimation. However, the decimation operation causes the degraded MS and PAN images to be slightly misaligned with each other and with the original MS image, which acts as the reference image in the fusion evaluation. In this study, two image fusion methods based on decimated and undecimated multiresolution analysis techniques were evaluated on three popular types of test data sets consisting of spatially degraded IKONOS MS and PAN images. In the experiment, image misalignments caused by decimation significantly influenced the quality of fused images and resulted in untrustworthy performances of the image fusion methods being evaluated. It was demonstrated that unlike aligned MS and PAN images in actual image fusion, misaligned MS and PAN images in test data sets are inappropriate for image fusion evaluation.

Context-adaptive pansharpening algorithm for high-resolution satellite imagery

Pansharpening algorithms are important methods for overcoming the technical limitations of satellite sensors. However, most approaches to pansharpening have tended either to distort the spectral characteristics of the original multispectral image or reduce the visual sharpness of the panchromatic image. In this paper, we propose a pansharpening algorithm that uses both a global and a local context-adaptive parameter based on component substitution. The purpose of this algorithm is to produce fused images with spectral information similar to that of multispectral data while preserving spatial sharpness better than other algorithms such as the Gram–Schmidt algorithm, Additive Wavelet Luminance Proportional algorithm, and algorithms developed in our previous work. The proposed parameter is calculated using the spatial and spectral characteristics of an image. It is derived from a spatial correlation between each multispectral band and adjusted-intensity image based on Laplacian filtering and statistical ratios, and the parameter is subsequently adaptively adjusted using image entropy to optimize the fusion quality in terms of the sensor and image characteristics. An experiment using IKONOS-2, QuickBird-2, and Geoeye-1 imagery demonstrated that a global context-adaptive parameter model is effective for spatial enhancement and that a local context-adaptive model is useful for image visualization and spectral information preservation.

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.

IKONOS uydu görüntüleri ile yeni bir görüntü kaynaştırma yöntemi

A novel image fusion method using IKONOS satellite images In satellite remote sensing, spatial resolutions of multispectral images over a particular region can be enhanced using better spatial resolution panchromatic images for the same region by a process called image fusion, or more generally data fusion. A fusion method is considered successful, if the spatial detail of the panchromatic image is transferred into the multispectral image and the spectral content of the original multispectral image is preserved in the fused product. This research proposes a novel image fusion algorithm which takes aim at producing both spatially enhanced and spectrally appealing fused multispectral images. In the proposed method, first an intermediary image is created using original panchromatic and multispectral images. This intermediary image contains the high frequency content of the panchromatic source image such that it is the one closest to the given multispectral source image (upsampled) by a natural semi inner product defined. The final fused image is obtained by applying a function which performs convex linear combination of the intermediary image and the upsampled multispectral image. The function used depends on the local standard deviations of the source images.To test the performance of the method, the images from IKONOS sensor are fused using the Brovey, IHS, PCA, wavelet transform based methods, and the proposed method. Both visual and quantitative evaluation results indicate that the proposed method yields to both spectrally and spatially appealing results as the wavelet transform based method, and it gives a better performance when both spatial detail enhancement and spectral content preservation in the fused products are considered. It is also obvious that the method has a potential to get better results if a better fitting, more complex function is found.

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.

Spectral and Spatial Quality Analysis in Pan Sharpening Process

Image fusion is a process to obtain new images containing more information by combining images obtained same or different sensors. With most of the earth observation satellites, high spatial resolution panchromatic images and low spatial resolution multispectral images are obtained. As an example of image fusion “pan sharpening” is a process of combining of high spatial resolution panchromatic images and low spatial resolution multispectral images. At the end of the fusion process both high spatial and spectral resolution new images are obtained. In this study, panchromatic and multispectral images gathered from Ikonos were used. Panchromatic and multispectral images belonging to the same sensor were combined by using different image fusion methods. As pan sharpening methods Brovey transform, Modified IHS, Principal Component Analysis (PCA), Wavelet PC transform and Wavelet A Trous transformation methods were used. Quality of fused products was evaluated from the point of view of both visual and statistical criteria. While wavelet based methods are succesfull in terms of protection of spectral quality of original multispectral images, the colorbased and statistical methods are giving better results within the improvement of spatial content.

Target Detection of Multi-spectral Image Based on PCA and ICA

Spectral features of various materials are different from each other,so the target can be detected among a group of ones with different materials based on the principle.Usually,the targets with the same features,such as color and shape, can not be detected in the panchromatic images;or the traditional methods can not give approving results because of the large scale of data and low precision of detection.So,this paper presents a new target detection method for multispectral images and hyperspectral images based on the Principal component analysis (PCA) and Independent component analysis(ICA).The data of multispectral images is firstly processed by PCA method in order to reduce the dimension of original multispectral images and take the redundancies out for getting the main components of images.The disposed data will be processed using the independent component analysis(ICA) algorithm,and then the target can be detected from the abstracted spectral features.This paper takes the advantages of PCA method and ICA method,and makes the target detection in multispectral and hyperspectral images quickly.At last,the validity of this method is verified by a test which detects two kinds of leaves(artificial and real),using the new method by combining the ICA and PCA.

Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations

This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery, based on principal component analysis (PCA) and intensity-hue-saturation (IHS) transformations. The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component, respectively. The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation, and undertaking the inverse IHS transformation. The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image. On the basis of the visual and statistical analysis results of the experimental study, we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion. It has potential advantages in image mapping optimization, object recognition, and weak information sharpening.

공간 상관도 기법에 따른 하이브리드 융합영상의 공간/분광 왜곡 평가

원격탐사 분야에서 고해상도의 멀티스펙트럴 영상은 위성센서의 기술적인 제약 때문에 직접적으로 취득하기 어렵다. 이러한 문제를 극복하기 위하여 다양한 위성영상 융합 기법이 제안되어졌다. 그러나, 대부분의 영상융합기법들은 원 멀티스펙트럴 영상의 분광정보를 왜곡시키거나 고해상도 흑백영상의 공간해상도에 비하여 융합영상의 공간해상도가 저하되는 문제점을 지닌다. 최근 들어 융합영상의 분광왜곡을 최소화시키면 서 공간해상도를 효율적으로 증가시킬 수 있는 공간상관도 기반의 하이브리드 융합 기법이 제안되었다. 본 연구에서는 하이브리드 융합기법에 다양한 공간상관도 추출 식을 적용하여 보고 이에 따른 분광/공간정보 왜곡의 양을 비교 평가하였다. 실험 결과 라플라시안 필터를 이용한 하이브리드 융합 기법이 다른 고주파 정보 추출 기법에 비하여 하이브리드 융합 기법에 효과적으로 적용할 수 있다는 것을 확인할 수 있었다. In remote sensing, it has been a difficult task to obtain a multispectral image with high spatial resolution because of the technical limitation of satellite sensors. In order to solve these problems, various pansharpening algorithms have been tried and proposed. However, most pansharpened images created by various approaches tend to distort the spectral characteristics of the original multispectral image or decrease the visual sharpness of the panchromatic image. To minimize the spectral distortion of pansharpened image while preserving spatial information of the panchromatic image, a hybrid pansharpening algorithm based on the spatial correlation was proposed. In this paper, we analyzed the spatial and spectral distortion of the hybrid pansharpened images generated by the various spatial correlation methods. In the experiments, we proved that the method by using Laplacian filtering was more efficient than other high frequency extraction algorithms in the viewpoint of spectral distortion and spatial sharpness.

Lossless and lossy coding for multispectral image based on sRGB standard and residual components

In this paper, we present a multispectral image (MSI) compression method using a lossless and lossy coding scheme, which focuses on the seamless coding of the RGB bit stream to enhance the usability of the MSI. The proposed method divides the MSI data into two components: RGB and residual. The RGB component is extracted from the MSI by using the XYZ color matching functions, a color conversion matrix, and a gamma curve. The original MSI is estimated by an RGB data encoder and the difference between the original and the estimated MSI, which is referred to as the residual component in this paper. Next, the RGB and residual components are encoded by using JPEG2000, and progressive decoding is achieved from the losslessly encoded code stream. Experimental results show that a high-quality RGB image can be obtained at a low bit rate with primary encoding of the RGB component. In addition, by using the proposed method, the quality of a spectrum can be improved by decoding the residual data, and the quality is comparable to that obtained by using JPEG2000. The lossless compression ratio obtained by using this method is also similar to that obtained by using JPEG2000 with the integer Karhunen-Loeve transform.