代写范文

留学资讯

写作技巧

论文代写专题

服务承诺

资金托管
原创保证
实力保障
24小时客服
使命必达

51Due提供Essay,Paper,Report,Assignment等学科作业的代写与辅导,同时涵盖Personal Statement,转学申请等留学文书代写。

51Due将让你达成学业目标
51Due将让你达成学业目标
51Due将让你达成学业目标
51Due将让你达成学业目标

私人订制你的未来职场 世界名企,高端行业岗位等 在新的起点上实现更高水平的发展

积累工作经验
多元化文化交流
专业实操技能
建立人际资源圈

美国作业代写:Remote sensing image Data fusion technology

2017-09-12 来源: 51due教员组 类别: 更多范文

下面为大家整理一篇优秀的assignment代写范文- Remote sensing image Data fusion technology,供大家参考学习,这篇论文讨论了遥感影像数据融合技术。由于技术条件的限制和成像原理的不同,单一遥感器的遥感数据都不会全面的反映目标特征。因此,将不同特征的数据结合起来,既可以发挥各单一遥感数据的优势,又可以弥补不足,相互取长补短。数据融合是对多遥感器的图像数据和其他信息的处理过程。它是按照一定的规则和算法,去除掉时间或空间上冗余或互补的多源数据,使其获得比任意单一数据更精确、更丰富信息的新合成图像。

Remote sensing Image,遥感影像,assignment代写,paper代写,留学生作业代写

With the development of remote sensing technology, the data of multi-sensor, multi-resolution and multiple-phase are attacked, each has its own advantages and limitations. How to fully develop and utilize these data resources, remote sensing data fusion provides us with the convenience. In this paper, three methods and fusion processes of remote sensing image data fusion are described.

Today, the rapid development of remote sensing technology, SPOT, LANDSAT/TM, SAR and other remote sensors, providing us with a large number of different phases, different bands, different angles, different resolution of remote sensing images. Because of the limitation of technical conditions and the different imaging principles, the remote sensing data of a single remote sensor will not fully reflect the target characteristics. Therefore, the combination of different characteristics of data, not only can play the advantages of a single remote sensing data, but also can make up for deficiencies, mutual complementarity.

Data fusion is the process of processing image data and other information of multiple remote sensors. It is based on certain rules and algorithms to eliminate the time or space redundant or complementary multi-source data, so that it is more accurate than any single data, more abundant information of new synthetic images.

Prior to fusion, a variety of single data must first be geometric correction, radiation correction, removal of bad line bands. Because of different characteristics of remote sensing image data from different sources, such as platform, observation angle, orbit and so on, it is necessary to make space registration, that is, to guarantee the image of the same region to be converted to the unified coordinate system. The second is to ensure the data association, that is, all kinds of data into a unified expression form.

Image data fusion based on pixels refers to the merging of the physical parameters of the measurement, that is, the fusion of the collected raw data directly. The fusion based on pixels often has some blindness, but because of the original image data, it can preserve the original real sense of the image, and provide the subtle information that other fusion levels can not provide.

Feature based image data fusion refers to the feature extraction using different algorithms and target recognition for various data sources. The fusion based on the feature level emphasizes the correspondence between the features, does not highlight the correspondence between the pixels, and avoids the human error caused by resampling in the process. But because it is not based on the original image data but the characteristics, it is unavoidable to lose some of the information in the feature extraction process, which makes it difficult to provide subtle information.

Image data fusion based on decision-making is the fusion of image recognition and image identification, which is based on feature extraction and feature recognition. It has a high degree of flexibility, the early processing requirements are high, is a high-level fusion, often directly oriented to application, for decision support services.

In the image data fusion, two kinds of his technique are used mainly. One is the direct method, the 3-band image will be transformed directly to the specified his space. The second is the substitution method, which first transforms the data set composed of the RGB3 band data into the separated his color space using the above formula.

Principal component Analysis is a method of removing the redundant information between bands, compressing the multi-band image information to a few conversion bands that are more effective than the original band. That is, in the case that the information is not lost as far as possible, several synthetic bands are used to represent the original image of the multi-band, reducing the amount of data.

There are two transformation methods in the fusion of principal component transformations, one is direct method: it will participate in the transform bands. Including high spatial resolution data, the principal component transform is unified, then the inverse principal component transform. The other is the substitution method: the multiple bands of multispectral are first transformed by the component, and similar to his transformation, the high-resolution panchromatic image is matched with the first principal component, which has the same mean value and variance with the first principal component, then replaces the first principal component with the high resolution image, and finally carries out the inverse principal component transform, The fusion image of multispectral images is improved by spatial resolution.

Because the same target is nonlinear between different data sources, nonlinear theory and model must be introduced. Wavelet transform has the advantages of information retention, zoom, and the flexibility of wavelet base selection. Therefore, since the 90 's, wavelet transform, as a new mathematical tool, has been hailed as a "mathematical microscope".

By wavelet transform, the image can be decomposed into some sub signals with different spatial resolution, directional characteristics and frequency characteristics. The fusion image can be obtained by using the high frequency component of high-resolution image data and the low frequency composition of the corresponding multispectral image data to reconstruct the wavelet. The image of wavelet transform can effectively enhance the spatial detail performance of multispectral images.

With the rapid development of remote sensing technology and application, the theory of remote sensing has been perfected and the method of remote sensing has been enriched and updated. Although there are many methods for the fusion of remote sensing image data, each method has its own limitations and it is difficult to establish a unified image fusion theory and method system. At present, the data fusion algorithm tends to combine the knowledge understanding and statistical information, and the feature fusion processing of multi-sensor or multi time data, and will be developed in the future.

51due留学教育原创版权郑重声明:原创assignment代写范文源自编辑创作,未经官方许可,网站谢绝转载。对于侵权行为,未经同意的情况下,51Due有权追究法律责任。主要业务有assignment代写、essay代写、paper代写服务。

51due为留学生提供最好的assignment代写服务,亲们可以进入主页了解和获取更多assignment代写范文 提供留学生作业代写服务,详情可以咨询我们的客服QQ:800020041。-ZR

上一篇:美国作业代写:Revocable Civil Conduct 下一篇:留学生作业代写:Feminist Music Researc