【摘要】：拍攝條件和天氣氣候等客觀因素的干擾造成的數(shù)據(jù)缺失,對(duì)光學(xué)遙感影像的解譯和應(yīng)用造成了巨大的限制。常見的數(shù)據(jù)缺失類型包括云層與云層陰影遮擋、條帶狀噪聲和其他噪聲。光學(xué)遙感圖像重建方法利用相關(guān)的圖像數(shù)據(jù),對(duì)圖像缺失部分的數(shù)據(jù)進(jìn)行還原,使得遙感圖像在主觀視覺上有著更高的辨識(shí)度,對(duì)后續(xù)諸如遙感圖像解譯、目標(biāo)檢測(cè)與監(jiān)督、地物分類和變化檢測(cè)等應(yīng)用帶來了極大的便利。目前,常用的光學(xué)遙感圖像重建算法可以分為基于輔助圖像信息的重建方法和基于圖像修復(fù)技術(shù)的重建方法。其中,基于輔助圖像信息的重建方法是指利用多光譜或多時(shí)域的圖像之間的相關(guān)性信息,對(duì)缺失區(qū)域進(jìn)行重建�；趫D像修復(fù)技術(shù)利用待修復(fù)圖像數(shù)據(jù)完好區(qū)域的圖像信息,對(duì)缺失區(qū)域進(jìn)行估計(jì)。文章所提出的算法基于圖像修復(fù)技術(shù)。這一類重建方法的瓶頸之一是缺乏待修復(fù)數(shù)據(jù)的圖像先驗(yàn)信息,制約了其重建準(zhǔn)確度�？紤]到微波圖像不受氣候影響,以及對(duì)云層的穿透性,文章利用常用的微波圖像聚類分類方法對(duì)微波遙感圖像進(jìn)行分割,得到其圖像結(jié)構(gòu)信息,提出一種結(jié)合微波圖像先驗(yàn)結(jié)構(gòu)信息的圖像修復(fù)方法,應(yīng)用于光學(xué)遙感圖像重建。實(shí)驗(yàn)證明,在海岸線的應(yīng)用場(chǎng)景下,文章提出的算法更好地保持了圖像結(jié)構(gòu)的連貫性,相較于幾種常用的重建方法,文章提出的算法具有更高的重建精度。文章所做主要研究工作內(nèi)容如下:1、基于Criminisi圖像修復(fù)算法,文章將其應(yīng)用于光學(xué)遙感圖像云層移除的具體重建問題,并使用微波圖像替換掉了原有算法中的光學(xué)亮度圖像,提出了一種基于微波遙感圖像的等高照線數(shù)據(jù)項(xiàng)計(jì)算方法,增強(qiáng)了原有算法對(duì)地表不規(guī)則結(jié)構(gòu)的重建精度。2、文章研究分析了常用的聚類圖像分類算法,對(duì)微波遙感圖像進(jìn)行了分割,分析比較了不同聚類算法之間的分割效果和各自的優(yōu)劣之處。針對(duì)海岸線應(yīng)用場(chǎng)景的重建問題,文章將分割后的微波圖像結(jié)果作為先驗(yàn)信息,提出一種先分類后重建的重建方法模式,在地表分界線類型區(qū)域的重建中,取得了良好的重建精度。與其他常用光學(xué)圖像重建方法相比,文章所得重建圖像與原始純凈圖像具有更好的相似程度。
[Abstract]:The lack of data caused by the interference of objective factors such as shooting conditions and weather and climate has greatly restricted the interpretation and application of optical remote sensing images. Common data loss types include cloud and cloud shadows, banded noise and other noises. The method of optical remote sensing image reconstruction uses the relevant image data to restore the missing part of the image, which makes the remote sensing image have higher recognition degree in subjective vision, such as interpretation of remote sensing image, target detection and supervision. The application of ground object classification and change detection has brought great convenience. At present, the commonly used optical remote sensing image reconstruction algorithms can be divided into auxiliary image information based reconstruction method and image restoration technology based reconstruction method. The reconstruction method based on auxiliary image information refers to the reconstruction of missing region by using the correlation information of multi-spectral or multi-time domain images. Based on the image restoration technique, the missing region is estimated by using the image information of the intact region of the image data to be repaired. The proposed algorithm is based on image restoration technology. One of the bottlenecks of this kind of reconstruction method is the lack of image prior information of the data to be repaired, which restricts its reconstruction accuracy. Considering that microwave images are not affected by climate and are penetrating to clouds, the microwave remote sensing images are segmented by the commonly used methods of microwave image clustering and classification, and their image structure information is obtained. An image restoration method based on the prior structure information of microwave image is proposed, which is applied to the reconstruction of optical remote sensing image. The experiments show that the algorithm proposed in this paper can better maintain the coherence of image structure in the application scene of shoreline. Compared with several commonly used reconstruction methods, the algorithm proposed in this paper has higher reconstruction accuracy. The main work of this paper is as follows: 1. Based on the Criminisi image restoration algorithm, this paper applies it to the reconstruction of optical remote sensing image cloud removal, and uses microwave image to replace the optical luminance image in the original algorithm. In this paper, a method of calculating the isometric line data item based on microwave remote sensing image is proposed, which enhances the reconstruction accuracy of the original algorithm to the irregular structure of the earth's surface. In this paper, the commonly used clustering image classification algorithm is studied and analyzed. The segmentation of microwave remote sensing image is carried out, and the segmentation effect of different clustering algorithms and their advantages and disadvantages are analyzed and compared. Aiming at the reconstruction problem of shoreline application scene, this paper takes the result of microwave image segmentation as a priori information, and proposes a reconstruction method mode of classification and reconstruction, which is used in the reconstruction of the surface boundary type area. Good reconstruction accuracy has been obtained. Compared with other commonly used optical image reconstruction methods, the reconstructed image has a better similarity with the original pure image.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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本文編號(hào)：2253152