【摘要】：坡度(Slope)是地形特征的重要參數(shù),也是地貌變化的重要評(píng)價(jià)指標(biāo)。區(qū)域尺度上的坡度,通常由DEM(Digital Elevation Model)計(jì)算獲得。由于高分辨率DEM數(shù)據(jù)獲取較困難,而低分辨率下,坡度數(shù)據(jù)的高頻和低頻部分損失嚴(yán)重,因此通過(guò)超分辨率重構(gòu)(降尺度變化)的方式來(lái)獲取高精度坡度數(shù)據(jù)成為解決問(wèn)題的途徑之一。本研究通過(guò)改進(jìn)的POCS(Projections Onto Convex Sets)算法和基于稀疏混合估計(jì)的方法對(duì)坡度數(shù)據(jù)的超分辨率重構(gòu)進(jìn)行研究,主要內(nèi)容如下:(1)通過(guò)改進(jìn)的POCS算法對(duì)坡度數(shù)據(jù)進(jìn)行超分辨率重構(gòu)。本算法首先針對(duì)讀入的多組DEM數(shù)據(jù)進(jìn)行判斷,根據(jù)每個(gè)數(shù)據(jù)的方差結(jié)果來(lái)確定本組數(shù)據(jù)值的大致范圍,對(duì)其中與平均范圍差異較大的數(shù)據(jù)進(jìn)行剔除,來(lái)獲得更好的重構(gòu)效果;利用雙線性插值的方法來(lái)構(gòu)造參考數(shù)據(jù),使結(jié)果更加平滑真實(shí);之后進(jìn)行參考數(shù)據(jù)修正,修正過(guò)程是根據(jù)梯田坡度變化較大的值,將坡度數(shù)據(jù)進(jìn)行邊緣檢測(cè),獲得檢測(cè)結(jié)果后與將其對(duì)參考數(shù)據(jù)進(jìn)行修正。(2)基于稀疏混合估計(jì)法對(duì)坡度數(shù)據(jù)進(jìn)行超分辨率重構(gòu)。算法首先將坡度數(shù)據(jù)、在近似、水平、垂直與45°四種方向上進(jìn)行小波變換,該步驟可以充分提取坡度數(shù)據(jù)特征。然后建立坡度數(shù)據(jù)塊字典和對(duì)塊進(jìn)行正交匹配追蹤,通過(guò)構(gòu)建坡度數(shù)據(jù)的稀疏冗余塊字典,對(duì)坡度數(shù)據(jù)特性的歸納,使數(shù)據(jù)作為幾何塊進(jìn)行正交匹配追蹤,通過(guò)幾何塊的方法可使數(shù)據(jù)的完整性得到保證,最后結(jié)合小波與塊字典對(duì)坡度數(shù)據(jù)進(jìn)行混合插值。稀疏混合估計(jì)法通過(guò)同時(shí)計(jì)算L1和L2范式,充分考慮到坡度數(shù)據(jù)的規(guī)律性變化,結(jié)合多方向小波變換在不同方向?qū)ζ露葦?shù)據(jù)進(jìn)行混合插值,保證了重構(gòu)數(shù)據(jù)的完整性和結(jié)果的準(zhǔn)確率。本研究以甘肅龍泉梯田區(qū)DEM為研究對(duì)象,由無(wú)人機(jī)航空攝影測(cè)量方法獲取高分辨率高程數(shù)據(jù),并從中提取坡度。利用改進(jìn)的POCS算法和稀疏混合估計(jì)算法針對(duì)DEM坡度數(shù)據(jù)進(jìn)行超分辨率重構(gòu),并與最近鄰法、雙線性插值法、三次卷積插值法比較與評(píng)估。結(jié)果表明本研究方法在空間分布和誤差方面上均優(yōu)于其它方法。在本研究的兩個(gè)算法結(jié)果比較中,稀疏混合估計(jì)法的結(jié)果要優(yōu)于改進(jìn)的POCS算法。
[Abstract]:Slope (Slope) is not only an important parameter of topographic features, but also an important evaluation index of geomorphologic change. Slopes on a regional scale are usually calculated by DEM (Digital Elevation Model). Because of the difficulty of obtaining high-resolution DEM data, and the loss of high-frequency and low-frequency parts of slope data at low resolution, Therefore, it is one of the ways to obtain high precision slope data by super-resolution reconstruction (downscaling change). In this study, the super-resolution reconstruction of slope data is studied based on the improved POCS (Projections Onto Convex Sets) algorithm and the sparse mixed estimation method. The main contents are as follows: (1) Super-resolution reconstruction of slope data is carried out through the improved POCS algorithm. In this algorithm, we first judge the multiple groups of DEM data read in, determine the approximate range of the data values according to the variance results of each data, and eliminate the data which is different from the average range, so as to obtain a better reconstruction effect. The method of bilinear interpolation is used to construct the reference data to make the results smoother and truer, and then the reference data are corrected. The correction process is to detect the edge of the slope data according to the large change of terrace slope. After obtaining the detection results and modifying them to the reference data. (2) the slope data is reconstructed by super-resolution based on sparse mixed estimation method. Firstly, wavelet transform is carried out on the slope data in approximate, horizontal, vertical and 45 擄directions. This step can fully extract the feature of slope data. Secondly, the dictionary of slope data block and the orthogonal matching tracing of the block are established. By constructing the sparse redundant block dictionary of slope data, the characteristic of slope data is induced, and the data is used as geometric block for orthogonal matching tracing. The integrity of the data can be guaranteed by geometric block method. Finally, the slope data is interpolated with wavelet and block dictionary. By calculating L _ 1 and L _ 2 normal forms at the same time, the sparse mixed estimation method takes full account of the regular variation of slope data, and combines multi-directional wavelet transform to interpolate gradient data in different directions. The integrity of the reconstructed data and the accuracy of the results are ensured. In this study, high resolution elevation data were obtained from aerial photogrammetry of unmanned aerial vehicle (UAV) and slope was extracted from DEM in Longquan terraced area of Gansu province. The improved POCS algorithm and sparse mixed estimation algorithm are used to reconstruct the DEM slope data in super-resolution, and compared with the nearest neighbor method, bilinear interpolation method and cubic convolution interpolation method. The results show that this method is superior to other methods in spatial distribution and error. In the comparison of the results of the two algorithms, the result of sparse mixed estimation is better than that of the improved POCS algorithm.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：P208

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