本文選題：遙感圖像 + 圖像去噪�。� 參考：《北京交通大學(xué)》2014年碩士論文

【摘要】：近年來,遙感圖像的區(qū)域變化檢測技術(shù)在國民經(jīng)濟和國防建設(shè)中發(fā)揮了重要的作用。本文圍繞著變化檢測技術(shù)中一些關(guān)鍵技術(shù)進行了研究,主要涉及：1)遙感圖像去噪；2)彩色遙感圖像分割和3)遙感圖像分類識別。 由于氣象的原因,在遙感圖像成像過程中有時會混有云霧噪聲。區(qū)域性云霧的存在會嚴(yán)重影響遙感圖像的判讀和分析。本文在考察含云遙感圖像中地物信息和噪聲信息的頻率分布特點的基礎(chǔ)上,提出了基于小波變換及HSI顏色空間的遙感圖像云霧噪聲去除的算法。此算法在較大程度上保留遙感圖像中有用信息的基礎(chǔ)上,較好的去除了遙感圖像中存在的云霧噪聲。 遙感圖像分割是實現(xiàn)區(qū)域變化檢測的前提條件之一。分割質(zhì)量的好壞,決定著區(qū)域變化檢測的成敗。本文基于統(tǒng)計學(xué)原理利用統(tǒng)計區(qū)域合并算法對彩色遙感圖像進行了多尺度分割。針對分割過程中存在的“過分割”問題,本文結(jié)合分割后的區(qū)域的LBP特征及邊緣特征,提出了一種基于閾值的區(qū)域合并算法。 良好的圖像分類識別技術(shù),是區(qū)域變化檢測結(jié)果準(zhǔn)確的保障。文章對傳統(tǒng)的視覺詞袋模型(Bag-Of-Visual-Words, BOVW)算法中“單詞”分配步驟進行了分析研究。針對傳統(tǒng)的BOVW算法中“單詞”在“硬分配”過程中存在的問題,采用了具有魯棒性的“軟分配”方法。利用此方法,可以提高遙感圖像的分類識別率。 傳統(tǒng)的BOVW算法在計算時間上消耗巨大。本文在對傳統(tǒng)的BOVW算法進行較為深入研究的基礎(chǔ)上,提出的Fast BOVW算法。此算法主要通過改進視覺詞典建立的方法,來達到提高計算速度的目的。通過在實際的數(shù)據(jù)庫上的實驗表明,本文提出的Fast BOVW算法在保證分類精度前提下能夠?qū)⒂嬎闼俣忍岣週(本文為20～30倍)左右。 本項研究取得的相關(guān)結(jié)果對提高遙感圖像區(qū)域發(fā)生變化的處理能力有一定的指導(dǎo)和借鑒意義。
[Abstract]:In recent years, regional change detection technology of remote sensing images has played an important role in national economy and national defense construction.This paper focuses on some key technologies of change detection, including: 1) denoising of remote sensing image (2) segmentation of color remote sensing image and 3) classification and recognition of remote sensing image.Due to meteorological reasons, cloud noise is sometimes mixed in remote sensing image imaging.The existence of regional cloud and fog will seriously affect the interpretation and analysis of remote sensing images.Based on the investigation of the characteristics of frequency distribution of ground object information and noise information in remote sensing images containing cloud, an algorithm for removing cloud noise from remote sensing images based on wavelet transform and HSI color space is proposed in this paper.On the basis of preserving the useful information in remote sensing image to a large extent, this algorithm can remove the cloud and fog noise in remote sensing image.Remote sensing image segmentation is one of the prerequisites for regional change detection.The quality of segmentation determines the success or failure of regional change detection.Based on the principle of statistics, the multi-scale segmentation of color remote sensing images is carried out by using the statistical region merging algorithm.Aiming at the problem of "over-segmentation" in the segmentation process, this paper proposes a threshold-based region merging algorithm combining the LBP features and edge features of the segmented regions.Good image classification and recognition technology is the guarantee of accurate regional change detection results.In this paper, the steps of word allocation in the traditional visual word bag model (Bag-Of-Visual-WordsBOVW) are analyzed and studied.Aiming at the problem of "word" in traditional BOVW algorithm in "hard assignment", a robust "soft assignment" method is adopted.By using this method, the classification and recognition rate of remote sensing images can be improved.The traditional BOVW algorithm consumes a lot of computation time.Based on the deep research of the traditional BOVW algorithm, this paper proposes the Fast BOVW algorithm.This algorithm can improve the speed of calculation by improving the method of visual dictionary building.The experiments on the actual database show that the proposed Fast BOVW algorithm can improve the computing speed by about 30 times (20 ~ 30 times in this paper) on the premise of ensuring the classification accuracy.The results obtained in this study are helpful to improve the processing ability of remote sensing images.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：1732658