本文選題：太陽活動(dòng)識(shí)別　切入點(diǎn)：圖像分割　出處：《昆明理工大學(xué)》2017年碩士論文

【摘要】：基于內(nèi)容的圖像檢索技術(shù)發(fā)展至今,已經(jīng)在天文圖像處理領(lǐng)域得到了廣泛應(yīng)用。隨著多波段太陽觀測技術(shù)的迅猛發(fā)展,所采集的太陽圖像分辨率越來越高,極大地促進(jìn)了太陽活動(dòng)研究的進(jìn)展。相應(yīng)地,海量天文數(shù)據(jù)的激增,也給天文圖像的數(shù)據(jù)存儲(chǔ)帶來了極大的挑戰(zhàn)。而如何從全日面圖像中自動(dòng)檢測和識(shí)別出有效的太陽活動(dòng),是天文圖像處理領(lǐng)域的另一個(gè)難題。本文基于以上兩個(gè)問題展開研究,包含以下三個(gè)方面:第一,本文提出了一種基于方形網(wǎng)格結(jié)構(gòu)的太陽活動(dòng)目標(biāo)檢測方法(GBTD)。此方法將太陽圖像劃分成等大小的方形網(wǎng)格結(jié)構(gòu),基于多閾值選取策略和GBTD策略分離出目標(biāo)區(qū)域和背景區(qū)域。通過對(duì)6種太陽活動(dòng),共2172個(gè)太陽活動(dòng)區(qū)域進(jìn)行分割實(shí)驗(yàn),結(jié)果表明,該方法在切割準(zhǔn)確度及時(shí)間開銷方面取得滿意的結(jié)果,對(duì)圖像噪聲具有良好的抗干擾性。GBTD方法為多種類型的太陽活動(dòng)的研究提供一種通用的圖像分割方法,也為解決海量天文數(shù)據(jù)存儲(chǔ)的難題提供了一種可行辦法。第二,對(duì)太陽圖像特征參數(shù)相關(guān)性的研究,得到了每種太陽活動(dòng)的最佳特征參數(shù)組合。對(duì)不同太陽活動(dòng)區(qū)域提取特定組合的特征,可以為基于內(nèi)容的圖像檢索系統(tǒng)(CBIRS)建立精簡的圖像特征集提供了一種可行辦法。第三,得益于美國太陽動(dòng)力學(xué)天文臺(tái)(SDO)的日球物理學(xué)事件知識(shí)庫(HEK)所提供的實(shí)時(shí)太陽觀測數(shù)據(jù),本文提出了一種基于日球物理學(xué)事件知識(shí)庫的太陽活動(dòng)識(shí)別方法。此方法獲取6種太陽活動(dòng)的信息(發(fā)生時(shí)間、位置、區(qū)域面積),建立對(duì)應(yīng)時(shí)間的全日面圖像的尺度變換模型。結(jié)合位置與區(qū)域面積信息,對(duì)不同太陽活動(dòng)進(jìn)行梯度閾值分割,邊界識(shí)別方法被用來定位和識(shí)別太陽活動(dòng)的區(qū)域。本文方法實(shí)現(xiàn)了對(duì)太陽活動(dòng)的精確定位和有效識(shí)別,為后續(xù)工作的開展提供了便利。
[Abstract]:Content-based image retrieval technology has been widely used in the field of astronomical image processing. With the rapid development of multi-band solar observation technology, the resolution of the collected solar image is getting higher and higher. It has greatly promoted the research progress of solar activity. Accordingly, the explosion of massive astronomical data has also posed a great challenge to the data storage of astronomical images. And how to automatically detect and recognize effective solar activity from all heliospheric images, It is another difficult problem in the field of astronomical image processing. Based on the above two problems, this thesis includes the following three aspects: first, In this paper, a method of detecting solar moving objects based on square grid structure is presented. This method divides the solar image into square grid structures of equal size. The target region and background area are separated based on multi-threshold selection strategy and GBTD strategy. 2172 solar active regions are segmented by six solar activities, and the results show that, The method obtained satisfactory results in terms of cutting accuracy and time cost. The method has good anti-interference to image noise. GBTD method provides a general image segmentation method for the study of various types of solar activity. It also provides a feasible way to solve the problem of storing massive astronomical data. Secondly, the correlation of the characteristic parameters of solar images is studied. The optimal combination of characteristic parameters for each solar activity is obtained. Extracting the features of specific combinations for different solar active regions provides a feasible method for building a simplified image feature set for content-based image retrieval system (CBIRS). Third, Benefiting from the real-time solar observation data provided by the knowledge Base on Solar Sphere Physics events of the United States Solar Dynamics Observatory (SDO), In this paper, a method of solar activity recognition based on the knowledge base of heliospheric physics events is proposed. In this paper, the scale transformation model of the whole heliospheric image corresponding to the time is established, and the gradient threshold segmentation of different solar activity is carried out by combining the information of position and area. The boundary recognition method is used to locate and identify the region of solar activity. In this paper, the accurate location and effective identification of solar activity are realized, which provides convenience for the subsequent work.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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