The influence of destabilizing factors in the high resolution multispectral imaging systems
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In this work, destabilizing factors affecting the quality and resolution of images, multispectral monitoring systems in the optical (visible and infrared) and microwave bands are analyzed and systematized. The analysis of the influence of destabilizing factors on the formation of images in multispectral monitoring systems allows us to take into account the described effects when creating of domestic highly effective multispectral monitoring systems.Keywords:
Multispectral pattern recognition
All the commercial satellites (SPOT, LANDSAT, IRS, IKONOS, Quickbird and Orbview) collect a high spatial resolution panchromatic image and multiple (usually four) multispectral images with significant lower spatial resolution. The PAN images are characterised by a very high spatial information content well-suited for intermediate scale mapping applications and urban analysis. The multispectral images provide the essential spectral information for smaller scale thematic mapping applications such as landuse surveys. Why don't most satellites collect high-resolution MS images directly, to meet this requirement for high-spatial and high-spectral resolutions? There is a limitation to the data volume that a satellite sensor can store on board and then transmit to ground receiving station. Usually the size of the panchromatic image is many times larger than the size of the multispectral images. The size of the panchromatic of Landsat ETM+ is four times greater than the size of a ETM+ multispectral image. The panchromatic image for IKONOS, Quickbird SPOT5 and Orbview is sixteen times larger than the respective multispectral images. As a result if a sensor collected high-resolution multispectral data it could acquire fewer images during every pass. Considering these limitations, it is clear that the most effective solution for providing high-spatial-resolution and high-spectral-resolution remote sensing images is to develop effective image fusion techniques. Image fusion is a technique used to integrate the geometric detail of a high-resolution panchromatic (Pan) image and the color information of a low-resolution multispectral (MS) image to produce a high-resolution MS image. During the last twenty years many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, IHS Transform have been developed producing good quality fused images. Despite the quite good optical results many research papers have reported the limitations of the above fusion techniques. The most significant problem is color distortion. Another common problem is that the fusion quality often depends upon the operator's fusion experience, and upon the data set being fused. No automatic solution has been achieved to consistently produce high quality fusion for different data sets. More recently new techniques have been proposed such as the Wavelet Transform, the Pansharp Transform and the Modified IHS Transform. Those techniques seem to reduce the color distortion problem and to keep the statistical parameters invariable. In this study we compare the efficiency of eight fusion techniques and more especially the efficiency of Multiplicative Brovey, IHS, Modified IHS, PCA, Pansharp, Wavelet and LMM (Local Mean Matching) fusion techniques for the fusion of Ikonos data. For each merged image we have examined the optical qualitative result and the statistical parameters of the histograms of the various frequency bands, especially the standard deviation All the fusion techniques improve the resolution and the optical result. The Pansharp, the Wavelet and the Modified IHS merging technique do not change at all the statistical parameters of the original images. These merging techniques are proposed if the researcher want to proceed to further processing using for example different vegetation indexes or to perform classification using the spectral signatures.
Panchromatic film
Multispectral pattern recognition
Thematic map
Thematic Mapper
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The shortcomings of the conventional three-channel imaging are rooted in metamerism problems. To avoid these problems multispectral imaging is introduced as an complementary approach. This imaging technology uses the complete spectral information of the original image, and, hence, avoids the problems due to metamerism. A multispectral image requires much more data than the same image using conventional technology (e.g. 31 sample values instead of 3 colorimetric values, corresponding to a data enlargement factor of about 10). Efficient multispectral image encoding has therefore been developed. Furthermore, this multispectral image encoding is compatible to conventional image encoding. The multispectral image encoding is based on the decomposition of the spectral data into three conventional components (e.g. RGB) and additional metameric components. Using this approach, the data enlargement factor can be reduced to values between 2 and 3.
Multispectral pattern recognition
RGB color model
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In this paper, a new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is in particular extremely suited for the fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experiments are performed on multispectral images. In these experiments, Landsat Thematic Mapper images are fused and merged with panchromatic images. The proposed techniques are compared to wavelet-based techniques described in the literature.
Panchromatic film
Multispectral pattern recognition
Representation
Thematic Mapper
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Multispectral remote sensing is one of the most popular techniques in the earth observation, because this technique can provide information of ground objects on Earth’s surface using hundreds of narrow bands. However, multispectral images produces a very large volume of data. Processing the huge volume of information is one of most important and actual problems of remote sensing. The rapid development of the remote sensing demand to develop the data processing algorithms. But at present data processing techniques cannot give accurate results. If we use traditional methods to process multispectral images, the volume of the data increases. The main goal of the band selection is to choose the optimal combination of spectral bands for the solution of the particular remote sensing task. This process is important because different bands are sensitive to different objects. Selecting the right bands can help to optimize the detection of different ground objects. Some spectral bands are more sensitive to minerals, while others are more sensitive to vegetation or water bodies. Under a small number of training samples, the classification accuracy of multispectral images decreases when the volume of multispectral data increases. Usually adjacent bands are highly correlated, and some spectral bands may not carry unique information. That’s why it is necessarily to reduce the dimensionality of multispectral data. It helps to store, process, transmit information more efficiently and to reduce the computational costs while processing images. The different modern methods of multispectral band selection are also considered and analyzed in this work. It also is proposed a new method to select spectral bands, which is based on the concept of criterion function of information capability of spectral bands. In this article some examples using criterion function of information capability are considered too.
Multispectral pattern recognition
Spectral bands
Data Processing
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Multispectral image compression has been identified as a critical technology area for future advanced land remote sensing systems. The nature of multispectral imagery is such that, with correlated spectral bands, additional redundancy exists between registered pixels which can be exploited for compression gains. Because of the stringent requirements placed upon multispectral compression by various exploitation activities, designing a multispectral compression algorithm is not trivial. This paper will concentrate, not on compression algorithms, but upon some of the system design requirements, image properties and other issues that face compression algorithm designers.< >
Multispectral pattern recognition
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A new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is, in particular, extremely suited for fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experiments are performed on multispectral images, where Landsat Thematic Mapper images are fused and merged with SPOT Panchromatic images. The proposed techniques are compared with wavelet-based techniques described in the literature.
Panchromatic film
Multispectral pattern recognition
Representation
Thematic Mapper
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Multispectral pattern recognition
Spectral Clustering
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Compressive sensing of noisy multispectral images is considered in this letter. Multispectral images in remote sensing applications are multichannel and inherently noisy. An approach using Bregman split method for optimization in both spatial and transform domains is proposed. The performance of the proposed algorithm is evaluated by comparing with other approaches. It is shown that the proposed algorithm performs favorably compared with other approaches with noisy multispectral images in experiments.
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Many remote-sensing satellites can obtain images in multispectral and panchromatic bands. By fusing low-resolution multispectral and high-resolution panchromatic images, one can obtain high-resolution multispectral images. In this paper, an image fusion algorithm based on image restoration is proposed to combine multispectral and panchromatic images. For remote-sensing satellites, the wavelength of the panchromatic band usually covers the wavelengths of the multispectral bands. This relationship between the two kinds of images is useful for fusion. In our approach, the low-resolution multispectral images are first resampled to the scale of the high-resolution panchromatic image. The relationship between these two kinds of images is then used to restore the resampled multispectral images. That is, the resampled multispectral images are modeled as the noisy blurred versions of the ideal multispectral images, and the high-resolution panchromatic image is modeled as a linear combination of the ideal multispectral images plus the observation noise. The ideal high-resolution multispectral images are then estimated based on the panchromatic and the resampled multispectral images. A closed-form solution of the fused images is derived here. Experiments show that the proposed fusion algorithm works effectively in integrating multispectral and panchromatic images.
Panchromatic film
Multispectral pattern recognition
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This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semantic-image space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.
Multispectral pattern recognition
Feature (linguistics)
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