遙感圖像亞像元定位方法的研究
發(fā)布時(shí)間：2019-07-07 20:42
【摘要】：隨著遙感技術(shù)的迅猛發(fā)展,遙感圖像已被廣泛的應(yīng)用在環(huán)境/資源管理、自然災(zāi)害監(jiān)測(cè)、農(nóng)業(yè)/植被規(guī)劃、公共安全等技術(shù)領(lǐng)域。然而,遙感圖像在數(shù)據(jù)獲取過(guò)程中受環(huán)境參數(shù)和傳感器分辨等因素影響,使得混合像元不可避免的存在。混合像元的存在限制了遙感圖像的空間分辨率。空間分辨率受限為土地覆蓋信息的提取帶來(lái)了極大的困難。因此,如何提高遙感圖像的空間分辨率成為遙感領(lǐng)域?qū)W者研究的熱點(diǎn)問(wèn)題之一。光譜解混技術(shù)雖然能夠獲得混合像元內(nèi)部各地物類別所占的比例,但卻無(wú)法知道各地物類別在混合像元內(nèi)具體的空間分布情況。而亞像元定位正是一項(xiàng)確定混合像元內(nèi)各地物類別具體空間分布的技術(shù),它使得地物空間分部信息在更高尺度下顯示�；谏鲜鰞�(nèi)容,本文在悉心學(xué)習(xí)并總結(jié)現(xiàn)有研究成果的基礎(chǔ)上,對(duì)遙感圖像亞像元定位展開(kāi)了深入的研究,主要研究?jī)?nèi)容如下:1、在現(xiàn)有亞像元/亞像元空間引力模型的亞像元定位(SSSAM)的基礎(chǔ)上對(duì)模型參數(shù)進(jìn)行分析。通過(guò)對(duì)已有的SSSAM的學(xué)習(xí)與分析可知,在SSSAM方法中,距離權(quán)值函數(shù)是描述空間相關(guān)性的關(guān)鍵所在。不同的距離權(quán)值函數(shù)將從不同的角度對(duì)空間相關(guān)性進(jìn)行詮釋,進(jìn)而影響著亞像元定位的精度。文中分別將常見(jiàn)的三種距離權(quán)值函數(shù):距離倒數(shù)模型、指數(shù)模型和高斯模型應(yīng)用到SSSAM中,并通過(guò)兩組實(shí)驗(yàn)對(duì)采用不同距離權(quán)值函數(shù)的SSSAM方法的定位效果進(jìn)行分析與比較,從而選擇出最優(yōu)的距離權(quán)值函數(shù)。實(shí)驗(yàn)結(jié)果表明:距離倒數(shù)模型定位效果最差,而指數(shù)模型與高斯模型相比,高斯模型定位精度略高,但其對(duì)模型參數(shù)變化較敏感。2、提出了一種基于立方卷積插值算法的亞像元定位方法。亞像元定位過(guò)程可以描述成以下兩個(gè)步驟:第一,對(duì)低分辨率圖像進(jìn)行超分辨率,得到具有概率隸屬度信息的高分辨率圖像;第二,按照概率值大小,來(lái)確定亞像元定位的最終結(jié)果,即亞像元尺度下的“硬分類”結(jié)果�；谶@一思想,文中利用立方卷積插值算法對(duì)低分辨率圖像進(jìn)行超分辨率,再對(duì)超分辨率結(jié)果進(jìn)行亞像元級(jí)別上的“硬分類”,從而得到亞像元尺度上的地物分類圖。實(shí)驗(yàn)表明:該方法具有較好的定位精度,同時(shí),無(wú)需訓(xùn)練樣本和迭代計(jì)算,是一種簡(jiǎn)單易實(shí)現(xiàn)的方法。3、提出了一種基于混合插值算法的亞像元定位方法。利用傳統(tǒng)經(jīng)典的插值算法進(jìn)行亞像元定位的方法雖然可行,但插值算法本身的邊緣模糊效應(yīng)不可避免的存在。為了避免圖像插值過(guò)程的邊緣模糊效應(yīng)對(duì)亞像元定位精度的制約,設(shè)計(jì)了一種新的插值算法,即對(duì)偶插值,并將其分別與雙線性插值算法和反距離權(quán)值插值算法結(jié)合,形成兩種不同的混合插值算法。利用兩種不同的混合插值算法分別對(duì)低分辨率分量圖進(jìn)行超分辨率,得到高空間分辨率下各亞像元屬于某一地物類別的概率值,再根據(jù)分量值約束信息進(jìn)行亞像元級(jí)別上的“硬分類”,獲得亞像元定位的最終結(jié)果。實(shí)驗(yàn)結(jié)果表明:與應(yīng)用單一插值算法相比,利用混合插值算法進(jìn)行亞像元定位能夠更好地保持圖像的邊緣特性,獲得更高的定位精度。本文主要對(duì)三種亞像元定位方法進(jìn)行研究。首先,將不同的距離權(quán)值函數(shù)應(yīng)用到SSSAM中,通過(guò)實(shí)驗(yàn)為SSSAM選擇最佳的距離權(quán)值函數(shù);然后,將傳統(tǒng)的圖像插值算法應(yīng)用于亞像元定位中,為亞像元定位技術(shù)的研究開(kāi)辟了一個(gè)可行的新途徑,該方法在不需要訓(xùn)練樣本和迭代計(jì)算的情況下實(shí)現(xiàn)亞像元定位;而基于混合插值算法的亞像元定位是在上一種新方法的基礎(chǔ)上提出的,該方法利用高低分辨率圖像之間的空間對(duì)偶特性來(lái)克服傳統(tǒng)插值算法所固有的邊緣模糊效應(yīng),進(jìn)一步提高了亞像元定位的精度。三種方法從不同的角度改善亞像元定位精度,對(duì)遙感圖像的應(yīng)用有著極其重要的意義。
文內(nèi)圖片：
圖片說(shuō)明：圖１．１遙感圖像中混合像元的處理流程逡逑目前為止，科研工作者己嘗試了多種光譜解混的方法，，如線性光譜解混模型［３］、基逡逑
[Abstract]:With the rapid development of remote sensing technology, remote sensing images have been widely used in the fields of environment/ resource management, natural disaster monitoring, agriculture/ vegetation planning and public safety. However, the remote sensing image is affected by environmental parameters and sensor resolution in the data acquisition process, so that the mixed image element is inevitable. The existence of the mixed image element limits the spatial resolution of the remote sensing image. The limitation of spatial resolution brings great difficulty to the extraction of land cover information. Therefore, how to improve the spatial resolution of the remote sensing image is one of the hot issues in the field of remote sensing. The spectral unmixing technique can obtain the proportion of the object categories in the mixed image elements, but it is not possible to know the specific spatial distribution of the object categories in the mixed image elements. And the sub-pixel location is a technique for determining the specific spatial distribution of the object class in the mixed image element, which makes the space segment information of the figure space be displayed at a higher scale. Based on the above-mentioned contents, this paper, on the basis of learning and summarizing the existing research results, has carried out an in-depth study on the location of the sub-image elements of the remote sensing image. The main contents of this study are as follows:1. The model parameters are analyzed on the basis of the sub-pixel location (SSSAM) of the existing sub-pixel/ sub-pixel space gravity model. It can be seen from the study and analysis of the existing SSSAM that the distance weight function is the key to describing the spatial correlation in the SSSAM method. The different distance weights function will interpret the spatial correlation from different angles, thus affecting the accuracy of the sub-pixel location. In this paper, the common three distance weight functions: the distance reciprocal model, the exponential model and the Gaussian model are applied to the SSSAM, and the positioning effect of the SSSAM method with different distance weight functions is analyzed and compared by two groups of experiments, so that the optimal distance weight function is selected. The experimental results show that the positioning effect of the distance reciprocal model is the worst, and the exponential model is slightly higher than that of the Gaussian model, but it is sensitive to the variation of the model parameters. the sub-pixel positioning process can be described as two steps: first, the super-resolution of the low-resolution image is performed to obtain a high-resolution image with the probability membership information; secondly, the final result of the sub-pixel positioning is determined according to the probability value size, I. e., the "hard classification" results at the sub-pixel scale. Based on this idea, the super-resolution of the low-resolution image is carried out by the cubic convolution interpolation algorithm, and the "hard classification" of the sub-image element level is carried out on the super-resolution result, so as to obtain the ground object classification map on the sub-image element scale. The experiment shows that the method has better positioning accuracy, and is a simple and easy-to-implement method without training samples and iterative calculation. Although the traditional interpolation algorithm is feasible, the edge-fuzzy effect of the interpolation algorithm is inevitable. In order to avoid the restriction of the edge-fuzzy effect of the image interpolation process on the positioning accuracy of the sub-image element, a new interpolation algorithm, that is, the dual interpolation, is designed and combined with the bilinear interpolation algorithm and the inverse distance weight interpolation algorithm to form two different mixed interpolation algorithms. the method comprises the following steps of: respectively carrying out super-resolution on a low-resolution component image by using two different mixed interpolation algorithms to obtain a probability value of each sub-image element belonging to a certain property class at high spatial resolution, and then carrying out "hard classification" at the sub-image element level according to the component value constraint information, The final result of the sub-pixel location is obtained. The experimental results show that the edge characteristics of the image can be better preserved by using the mixed interpolation algorithm compared with the application of a single interpolation algorithm, and the higher positioning accuracy is obtained. In this paper, three kinds of sub-pixel location methods are studied. First, the different distance weight functions are applied to the SSSAM, and the best distance weight function is selected for the SSSAM through the experiment; then, the traditional image interpolation algorithm is applied to the sub-image element positioning, and a feasible new way is opened for the research of the sub-image element positioning technology, the method realizes the sub-pixel location without the need of training samples and iterative calculation, and the sub-pixel location based on the mixed interpolation algorithm is proposed on the basis of the previous novel method, The method uses the space dual property between high and low resolution images to overcome the edge fuzzy effect inherent in the traditional interpolation algorithm, and further improves the accuracy of the sub-pixel positioning. It is of great significance for the application of remote sensing image to improve the positioning accuracy of the sub-image element from different angles.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751
本文編號(hào)：2511430
文內(nèi)圖片：
圖片說(shuō)明：圖１．１遙感圖像中混合像元的處理流程逡逑目前為止，科研工作者己嘗試了多種光譜解混的方法，，如線性光譜解混模型［３］、基逡逑
[Abstract]:With the rapid development of remote sensing technology, remote sensing images have been widely used in the fields of environment/ resource management, natural disaster monitoring, agriculture/ vegetation planning and public safety. However, the remote sensing image is affected by environmental parameters and sensor resolution in the data acquisition process, so that the mixed image element is inevitable. The existence of the mixed image element limits the spatial resolution of the remote sensing image. The limitation of spatial resolution brings great difficulty to the extraction of land cover information. Therefore, how to improve the spatial resolution of the remote sensing image is one of the hot issues in the field of remote sensing. The spectral unmixing technique can obtain the proportion of the object categories in the mixed image elements, but it is not possible to know the specific spatial distribution of the object categories in the mixed image elements. And the sub-pixel location is a technique for determining the specific spatial distribution of the object class in the mixed image element, which makes the space segment information of the figure space be displayed at a higher scale. Based on the above-mentioned contents, this paper, on the basis of learning and summarizing the existing research results, has carried out an in-depth study on the location of the sub-image elements of the remote sensing image. The main contents of this study are as follows:1. The model parameters are analyzed on the basis of the sub-pixel location (SSSAM) of the existing sub-pixel/ sub-pixel space gravity model. It can be seen from the study and analysis of the existing SSSAM that the distance weight function is the key to describing the spatial correlation in the SSSAM method. The different distance weights function will interpret the spatial correlation from different angles, thus affecting the accuracy of the sub-pixel location. In this paper, the common three distance weight functions: the distance reciprocal model, the exponential model and the Gaussian model are applied to the SSSAM, and the positioning effect of the SSSAM method with different distance weight functions is analyzed and compared by two groups of experiments, so that the optimal distance weight function is selected. The experimental results show that the positioning effect of the distance reciprocal model is the worst, and the exponential model is slightly higher than that of the Gaussian model, but it is sensitive to the variation of the model parameters. the sub-pixel positioning process can be described as two steps: first, the super-resolution of the low-resolution image is performed to obtain a high-resolution image with the probability membership information; secondly, the final result of the sub-pixel positioning is determined according to the probability value size, I. e., the "hard classification" results at the sub-pixel scale. Based on this idea, the super-resolution of the low-resolution image is carried out by the cubic convolution interpolation algorithm, and the "hard classification" of the sub-image element level is carried out on the super-resolution result, so as to obtain the ground object classification map on the sub-image element scale. The experiment shows that the method has better positioning accuracy, and is a simple and easy-to-implement method without training samples and iterative calculation. Although the traditional interpolation algorithm is feasible, the edge-fuzzy effect of the interpolation algorithm is inevitable. In order to avoid the restriction of the edge-fuzzy effect of the image interpolation process on the positioning accuracy of the sub-image element, a new interpolation algorithm, that is, the dual interpolation, is designed and combined with the bilinear interpolation algorithm and the inverse distance weight interpolation algorithm to form two different mixed interpolation algorithms. the method comprises the following steps of: respectively carrying out super-resolution on a low-resolution component image by using two different mixed interpolation algorithms to obtain a probability value of each sub-image element belonging to a certain property class at high spatial resolution, and then carrying out "hard classification" at the sub-image element level according to the component value constraint information, The final result of the sub-pixel location is obtained. The experimental results show that the edge characteristics of the image can be better preserved by using the mixed interpolation algorithm compared with the application of a single interpolation algorithm, and the higher positioning accuracy is obtained. In this paper, three kinds of sub-pixel location methods are studied. First, the different distance weight functions are applied to the SSSAM, and the best distance weight function is selected for the SSSAM through the experiment; then, the traditional image interpolation algorithm is applied to the sub-image element positioning, and a feasible new way is opened for the research of the sub-image element positioning technology, the method realizes the sub-pixel location without the need of training samples and iterative calculation, and the sub-pixel location based on the mixed interpolation algorithm is proposed on the basis of the previous novel method, The method uses the space dual property between high and low resolution images to overcome the edge fuzzy effect inherent in the traditional interpolation algorithm, and further improves the accuracy of the sub-pixel positioning. It is of great significance for the application of remote sensing image to improve the positioning accuracy of the sub-image element from different angles.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751
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