本文選題：衛(wèi)星云圖　切入點(diǎn)：基于內(nèi)容的云圖檢索　出處：《寧波大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：氣象衛(wèi)星技術(shù)的迅速發(fā)展帶來(lái)了氣象云圖數(shù)據(jù)的爆炸性增長(zhǎng),傳統(tǒng)依靠人工標(biāo)注文本檢索的云圖檢索方法越來(lái)越不能滿足氣象需求�；趦�(nèi)容的圖像檢索作為一種管理海量圖像數(shù)據(jù)的檢索技術(shù)表現(xiàn)出非常高效的檢索性能,本文在分析衛(wèi)星云圖特點(diǎn)的基礎(chǔ)上結(jié)合基于內(nèi)容的圖像檢索技術(shù)進(jìn)行了基于內(nèi)容的云圖檢索的關(guān)鍵技術(shù)與應(yīng)用研究,主要工作內(nèi)容如下:(1)基于分?jǐn)?shù)階達(dá)爾文粒子群優(yōu)化算法(FODPSO)與FCM聚類算法的云層信息提取研究。傳統(tǒng)FCM聚類方法進(jìn)行云層信息提取時(shí),算法的收斂容易受初始聚類中心的影響,陷入局部最優(yōu)解。FODPSO采用自然選擇機(jī)制全局尋找最優(yōu)解,能極大的避免陷入局部最優(yōu)值,利用全局尋優(yōu)性能非常好的分階達(dá)爾文粒子群優(yōu)化算法優(yōu)化模糊C均值初始聚類中心。并用改善初始聚類中心的模糊C均值聚類算法進(jìn)行云系信息的提取,為下階段有效特征的提取做好準(zhǔn)備。(2)多通道衛(wèi)星云圖云圖融合研究。不同通道獲取的云圖蘊(yùn)含了不同天氣特征,融合多通道的云圖能夠形成包含多種天氣特征信息的云圖,利用這樣的云圖進(jìn)行檢索能夠匹配出更加準(zhǔn)確的歷史相似云圖。本文提出基于NSST與自適應(yīng)PCNN相結(jié)合的方法進(jìn)行紅外和可見(jiàn)光衛(wèi)星云圖融合。實(shí)驗(yàn)結(jié)果表明融合云圖中云系特征明顯,含有豐富的邊緣、紋理細(xì)節(jié)信息,具有更高的清晰度,蘊(yùn)含了更多的氣象信息。運(yùn)用融合云圖進(jìn)行檢索,能獲得比單一類型云圖更好的檢索效果。(3)準(zhǔn)確有效表示云圖“內(nèi)容”的特征提取研究�；趦�(nèi)容的云圖檢索技術(shù)的基礎(chǔ)是提取能夠表示云圖“內(nèi)容”的有效特征。根據(jù)云圖固有的特性,在提取云層信息的基礎(chǔ)上提取灰度、紋理和形狀三種可靠的底層特征表示云圖“內(nèi)容”�；叶忍卣骼迷茍D的灰度直方圖方法提取,紋理特征采用具有良好抗噪聲和灰度平移不變的LTrP算子提取。形狀特征采用具有尺度、旋轉(zhuǎn)和平移不變性的Krawtchouk矩來(lái)提取。(4)云圖多特征決策融合檢索研究。在多特征決策融合檢索過(guò)程中,特征權(quán)重的選擇直接影響著檢索的準(zhǔn)確性,傳統(tǒng)通過(guò)人工分配不同特征權(quán)重進(jìn)行融合的方法需要進(jìn)行非常多次的實(shí)驗(yàn)才可能找到比較好的檢索效果,但隨著融合特征種類的增多,人工權(quán)重的確定會(huì)越來(lái)越變得沒(méi)有效率。本文依據(jù)不同特征檢索結(jié)果的相似度量得分排序曲線下的面積作為特征權(quán)重,從而自適應(yīng)確定每種特征的權(quán)重。本文研究表明特征權(quán)重的大小與相似性度量排序得分曲線下的面積呈負(fù)相關(guān),越好的特征其得分曲線下的面積越小,越差的特征面積越大。
[Abstract]:The rapid development of meteorological satellite technology has brought explosive growth of meteorological cloud map data. The traditional cloud image retrieval method based on manual tagging text retrieval can not meet the meteorological requirements. As a retrieval technology for managing massive image data, content-based image retrieval has a very efficient retrieval performance. Based on the analysis of the characteristics of satellite cloud images, the key technologies and applications of content-based image retrieval are studied in this paper. The main work is as follows: (1) based on Fractional Darwin Particle Swarm Optimization (FODPSO) and FCM clustering algorithm, cloud information extraction is studied. The convergence of traditional FCM clustering algorithm is easily affected by the initial clustering center. Fall into the local optimal solution. FODPSO uses natural selection mechanism to find the global optimal solution, which can greatly avoid falling into the local optimal value. The fuzzy C-means initial clustering center is optimized by using the hierarchical Darwinian particle swarm optimization algorithm, which has very good global optimization performance, and the cloud information is extracted by using the fuzzy C-means clustering algorithm which improves the initial clustering center. To prepare for the next phase of effective feature extraction. 2) Multi-channel satellite cloud image fusion research. Different channels of cloud image contains different weather features, the fusion of multi-channel cloud image can form a cloud image containing a variety of weather characteristics. The retrieval of this kind of cloud image can match a more accurate historical similar cloud image. This paper presents a method of infrared and visible satellite cloud image fusion based on NSST and adaptive PCNN. The experimental results show that the fused cloud can be fused. The features of the cloud system are obvious in the picture. Contains rich edge, texture details, higher clarity, more meteorological information. The research on feature extraction for accurately and effectively representing "content" of cloud image can be obtained. The basis of content-based cloud image retrieval technology is to extract valid features that can represent "content" of cloud image. Based on the inherent characteristics of the cloud map, On the basis of extracting cloud information, three reliable underlying features, texture and shape, represent the "content" of the cloud image. The gray feature is extracted by using the gray histogram method of the cloud image. The texture feature is extracted by LTrP operator with good noise resistance and invariant gray level translation, and the shape feature is based on scale. In the process of multi-feature decision fusion retrieval, the selection of feature weights directly affects the accuracy of retrieval. The traditional method of fusion by manually assigning different feature weights requires a lot of experiments in order to find a better retrieval effect, but with the increase of the types of fusion features, The determination of artificial weights will become more and more inefficient. This study shows that the size of feature weight is negatively correlated with the area under the similarity measure ranking score curve, and the smaller the area under the score curve is, the bigger the feature area is.
【學(xué)位授予單位】：寧波大學(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)：1635027