本文選題：任務(wù)驅(qū)動　切入點(diǎn)：遙感影像檢索　出處：《武漢大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：遙感信息作為保障國家安全與國民經(jīng)濟(jì)建設(shè)的一種重要戰(zhàn)略性資源,在農(nóng)業(yè)、減災(zāi)等眾多問題的宏觀決策方面發(fā)揮著不可替代的作用。隨著對地觀測技術(shù)的不斷發(fā)展,遙感數(shù)據(jù)源將不斷豐富,面臨的關(guān)鍵問題之一是如何面向各類用戶的不同需求,提供有效的遙感影像檢索手段,實(shí)現(xiàn)遙感數(shù)據(jù)的快速獲取、處理及高效服務(wù)。目前遙感影像的獲取主要依賴于專業(yè)查詢訂購服務(wù)或者空間信息門戶,其中都要求用戶提交的查詢中包含對專業(yè)遙感信息不同詳細(xì)程度的定性或定量描述,不具備特定應(yīng)用領(lǐng)域的遙感信息語義查詢能力。 因此,本文提出任務(wù)驅(qū)動的遙感影像檢索概念,旨在通過遙感影像應(yīng)用任務(wù)智能檢索所需的遙感影像,簡化其獲取方式,提高其服務(wù)水平。然而,遙感影像應(yīng)用任務(wù)與遙感影像之間的關(guān)系由于時空地理環(huán)境的復(fù)雜性,較難抽象為一種通用的規(guī)則或模型。因此,本文引入基于案例的推理技術(shù),將遙感影像應(yīng)用任務(wù)與遙感影像之間的復(fù)雜時空關(guān)系隱藏在案例中,并通過類比推理來利用這種隱藏的關(guān)聯(lián),實(shí)現(xiàn)以遙感影像應(yīng)用任務(wù)為驅(qū)動智能檢索出所需的遙感影像。具體研究工作及成果如下： (1)提出了任務(wù)驅(qū)動的遙感影像檢索案例推理方法。該方法將難以抽象為規(guī)則的遙感影像應(yīng)用任務(wù)、空間、時間與遙感影像之間的關(guān)系使用案例來表示,通過類比推理來利用這種關(guān)系。用戶提交查詢后通過相似性檢索獲得已有相似遙感影像應(yīng)用案例,并對已有案例進(jìn)行適當(dāng)調(diào)整使之滿足用戶查詢,最終返回用戶所需的遙感影像。該方法較傳統(tǒng)基于規(guī)則、本體的方法,其知識源更加豐富,獲取難度更低,且更加容易表達(dá)特殊例外的情況。 (2)建立了以時間、空間、遙感影像應(yīng)用任務(wù)以及遙感影像為主體的遙感影像應(yīng)用案例語義表達(dá)模型。模型中的時間、空間、遙感影像應(yīng)用任務(wù)以及遙感影像等元素都使用本體定義和描述,并對每個元素進(jìn)行了特征建模,建立了其中的時空關(guān)系。模型包含概念模型和描述模型兩個層次,概念模型描述概念對象的本質(zhì)屬性和關(guān)系,描述模型則是從自然語言層次描述對象的屬性和關(guān)系。描述模型可通過語義推理實(shí)現(xiàn)向概念模型的轉(zhuǎn)化,概念模型和描述模型的結(jié)合既保證案例具有足夠的表達(dá)能力和推理能力,又搭建了從自然語言文本向計算機(jī)可理解文字轉(zhuǎn)化的橋梁。 (3)建立了基于深層語義的遙感影像應(yīng)用案例檢索模型,包括相似性度量模型和組織檢索模型。建立了遙感影像應(yīng)用案例中時間、空間、遙感影像應(yīng)用任務(wù)要素的局部相似性度量方法,以及這些元素構(gòu)成的案例整體相似性度量方法。在時間相似性上,基于時間結(jié)構(gòu)和遙感影像應(yīng)用任務(wù)特點(diǎn)建立了時間語義相似性度量模型。在空間相似性上,將空間對象實(shí)例之間的空間關(guān)系劃分為三類,并對應(yīng)給出了基于空間關(guān)系聯(lián)系強(qiáng)度、基于分類要素特征向量、基于圖譜的相似性計算方法。在遙感影像應(yīng)用任務(wù)相似性上給出了基于屬性的相似性度量模型。建立了顧及空間特征的案例檢索網(wǎng)絡(luò),擴(kuò)展了虛擬空間、時間索引節(jié)點(diǎn),空間索引節(jié)點(diǎn)使用雙層R樹實(shí)現(xiàn),時間索引節(jié)點(diǎn)使用倒排結(jié)構(gòu),且提出虛擬案例的概念改進(jìn)了原始擴(kuò)展激活算法,使得其top-k檢索性能與案例數(shù)量基本無關(guān)。該檢索網(wǎng)絡(luò)還支持不同用戶背景下案例元素動態(tài)權(quán)重的檢索。 (4)建立了基于知識的遙感影像應(yīng)用案例調(diào)整模型。該模型一方面利用已有的時間、空間、傳感器等本體領(lǐng)域知識作為調(diào)整知識,另一方面以案例庫自身作為訓(xùn)練集,從中挖掘差異調(diào)整規(guī)則和頻繁規(guī)則作為案例調(diào)整知識。其中重點(diǎn)對差異化調(diào)整規(guī)則的表達(dá)、學(xué)習(xí)算法進(jìn)行了闡述,提出了基于概念要素細(xì)分的遙感影像應(yīng)用案例差異化內(nèi)容表達(dá),以及顧及時空特征、語義特征的泛化準(zhǔn)則和基于規(guī)則網(wǎng)的泛化算法；最后給出了面向?qū)ο蟮暮筒樵凃?qū)動的調(diào)整知識應(yīng)用方式。 (5)建立了任務(wù)驅(qū)動的遙感影像檢索原型系統(tǒng)iGeoPortal,并在此基礎(chǔ)上開展了驗(yàn)證實(shí)驗(yàn),旨在驗(yàn)證本文提出的檢索方法的性能及有效性。同時給出了基于信息抽取的遙感影像查詢及案例的自然語言處理方法,建立了時空語義推理模型實(shí)現(xiàn)描述模型向概念模型的轉(zhuǎn)化,提出了以查詢?yōu)橹黧w的地名定位方法和基于求交的模糊地名空間范圍快速計算方法。初步試驗(yàn)表明,本文提出的任務(wù)驅(qū)動的遙感影像檢索CBR方法,及其中的關(guān)鍵技術(shù)是有效和可行的。
[Abstract]:The remote sensing information as an important strategic resource security, national security and national economic construction in agriculture, plays an irreplaceable role in macro decision of disaster reduction and many other issues. With the development of earth observation technology, remote sensing data source will be continuously enriched, one of the key issues facing is how different needs for various types of the user, provide effective remote sensing image retrieval method, to achieve quick access to remote sensing data processing and efficient service. At present, the acquisition of remote sensing image mainly depends on the professional or query subscription service portal, which requires users to submit a query to include professional remote sensing information qualitatively or quantitatively different detailed description and query of remote sensing the semantic information does not have a specific application ability.
Therefore, the remote sensing image retrieval task driven concept, aims to retrieval of remote sensing image required by the application of remote sensing image to simplify the task of intelligent access, improve the service level. However, due to the complexity of air environment when the relationship between the application of remote sensing images and remote sensing images of the task, difficult to abstract a general a rule or model. Therefore, this paper introduces the case-based reasoning technique, the complex spatial and temporal relationships between the application of remote sensing image task and remote sensing images hidden in the case, and by using the analogy to hide, to realize the application of remote sensing image task driven intelligent retrieval of remote sensing images is needed. The specific research work and the results are as follows:
(1) proposed the case-based reasoning method of remote sensing image retrieval. The method of task driving will be difficult to abstract rules of the application of remote sensing image task space, the relationship between the time and the use of remote sensing image case represented by analogical reasoning to exploit this relationship. The user submits the query by similarity retrieval has obtained similar images application of the case, and the case has been adjusted to satisfy the user query, remote sensing image eventually return required by the user. The method is based on the traditional rules, the method of ontology, the knowledge source is more abundant, the difficulty of obtaining lower, and more easily express special exceptions.
(2) established by time, space, expression model of remote sensing image semantic application case of the application of remote sensing images and remote sensing image as the main task. The model of time, space, the application of remote sensing images and remote sensing image task elements using ontology definition and description, and each of the elements of the feature modeling, established the relationship between time and space the model contains. The conceptual model and description model of two levels, the concept model to describe the concept of object attributes and relationship description model is the description of object attributes and relations from the level of natural language. The description model can realize the transformation to the conceptual model by combining semantic reasoning, conceptual model and description model not only guarantee the case with sufficient expression and reasoning ability, but also to build a bridge to understand text conversion from natural language to computer.
(3) the deep semantic retrieval model is established based on the case of the application of remote sensing images, including similarity measure model and organization model is established. Retrieval time, the application of remote sensing images in the case of space, application of remote sensing image task elements local similarity measure method, and these elements constitute the whole case similarity measure method. In the similar time on the time structure and the application of remote sensing images based on the characteristics of the establishment of task time semantic similarity measure model. The spatial similarity, the space between an instance of the object spatial relations are divided into three categories, and the corresponding spatial relations is presented based on the contact strength calculation based on the classification of feature vector elements, similarity based on mapping method. In the application of remote sensing image similarity task gives the measurement model based on similarity attribute was established. Considering spatial characteristics of case retrieval network expansion Virtual space, time index node, node using double R tree spatial index, time index node using an inverted structure, and put forward the concept of virtual case improved the original extended activation algorithm, the retrieval performance of Top-k and the number of cases is independent. The network also supports the retrieval of different users under the background of case elements of dynamic weight retrieval.
(4) to establish the application of remote sensing image case adjustment model based on knowledge. On the one hand, the model uses the existing time and space, such as the adjustment of sensor knowledge domain ontology knowledge, on the other hand the case base itself as the training set, mining frequent difference adjustment rules and rules from as the case to adjust the knowledge which focus on the expression. On the differential adjustment rules, learning algorithm is discussed, put forward the concept of the application of remote sensing image segmentation element case expression based on the difference in content, and take into account the temporal and spatial features, semantic features and generalization criteria based on generalization algorithm rules; finally, the object oriented and query driven adjustment are given. The application of knowledge
(5) the establishment of remote sensing image retrieval task driven iGeoPortal prototype system, and carried out on the basis of experiment, to verify the proposed retrieval performance and effectiveness. At the same time gives the Natural Language Processing method of remote sensing image query and case information extraction based on established spatio-temporal semantic reasoning model description model to achieve transformation the conceptual model, put forward methods to query the placename location as the main body and the fast calculation of space intersection fuzzy placename method based on remote sensing images. Experiment results show that the proposed task driven retrieval method of CBR, and the key technology is effective and feasible.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：P237

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