【摘要】：本文通過對不同時(shí)期武漢市遙感影像資料進(jìn)行預(yù)處理提取沙湖影像資料,并通過采用多尺度分割和決策樹分類的方法對影像資料進(jìn)行分類處理,利用智能手機(jī)等移動(dòng)終端來展示湖泊歷史的變遷情況,為湖泊執(zhí)法人員檢測違規(guī)占湖、填湖的現(xiàn)象提供一種快速、準(zhǔn)確、便捷的辦法,進(jìn)一步提高執(zhí)法人員的執(zhí)法效率,同時(shí)也為違規(guī)填湖取證提供參考。本文用到的變化檢測方法為圖像分類比較法,研究主要分為兩個(gè)方面:遙感影像的預(yù)處理和面向?qū)ο蟮挠跋穹诸?在移動(dòng)端檢測分類后影像的變化區(qū)域。主要研究內(nèi)容和創(chuàng)新之處包括以下幾方面:1、研究遙感影像的預(yù)處理技術(shù)。本文通過對湖北省地理信息中心獲取的航空影像資料進(jìn)行幾何糾正、影像鑲嵌、影像裁剪的處理,來獲取武漢市沙湖的影像資料,并經(jīng)過重采樣使得歷年的影像資料具有相同的分辨率(1M)。2、多尺度分割方法中最優(yōu)分割尺度。在通過采用多尺度分割方法對影像進(jìn)行面向?qū)ο筮b感影像分類時(shí),尺度參數(shù)的選取對分類結(jié)果的影響很大,不同的分割尺度對影像進(jìn)行分割時(shí)得到的對象大小不同且數(shù)量不等,尺度越小生成的對象越多,會造成過度分割的現(xiàn)象,反之,尺度越大生成的對象越少,會導(dǎo)致分割不足。由于不同地物類別的光譜、紋理、結(jié)構(gòu)等特征存在差異,所以,不同的地物類別有不一樣的最優(yōu)分割尺度。本文基于對象內(nèi)部同質(zhì)性高且外部異質(zhì)性高的原則建立分割質(zhì)量函數(shù)。通過對不同尺度參數(shù)下的分割質(zhì)量函數(shù)值進(jìn)行比較,來判斷最優(yōu)分割尺度。3、基于多尺度分割和決策樹分類方法的研究與改進(jìn)。本文對多尺度分割和CART決策樹分類方法進(jìn)行深入的研究和分析。在傳統(tǒng)的多尺度分割方法上引入圖像分塊的概念,并提出兩種分塊的方法:基于影像“拼接線”分塊的方法和基于大尺度的多尺度分割的分塊方法。對子塊進(jìn)行多尺度分割和決策樹分類。以81年的沙湖影像數(shù)據(jù)為實(shí)驗(yàn)對象,把這兩種改進(jìn)的分類方法與傳統(tǒng)的多尺度分割和決策樹分類的影像分類結(jié)果進(jìn)行精度對比,結(jié)果顯示:基于分塊的多尺度分割和決策樹分類方法的精度明顯高于對整幅影像進(jìn)行多尺度分割和決策樹分類方法。其中基于大尺度的多尺度分塊方法精度最高。4、基于智能移動(dòng)終端的湖泊變遷區(qū)域檢測。簡要介紹武漢市水文化App系統(tǒng),同時(shí)在系統(tǒng)的變遷功能模塊中實(shí)現(xiàn)對分類后影像圖的變遷區(qū)域的檢測。
[Abstract]:In this paper, the sand lake image data are extracted by preprocessing the remote sensing image data of Wuhan in different periods, and the image data are classified and processed by using the method of multi-scale segmentation and decision tree classification. The use of mobile terminals such as smart phones to show the historical changes of lakes provides a fast, accurate and convenient way for lake law enforcement personnel to detect illegal occupation of lakes and fill lakes, and further improve the efficiency of law enforcement personnel. At the same time, it also provides a reference for evidence collection of illegal lake filling. The change detection method used in this paper is the image classification and comparison method. The research is mainly divided into two aspects: remote sensing image preprocessing and object oriented image classification; The main research contents and innovations include the following aspects: 1, the research of remote sensing image preprocessing technology. In this paper, the image data of Shahu Lake in Wuhan City is obtained through geometric correction, image mosaic and image tailoring of aerial image obtained by Hubei Geographic Information Center. After resampling, the image data have the same resolution (1m). 2, the optimal segmentation scale in the multi-scale segmentation method. When using multi-scale segmentation method to classify the object oriented remote sensing images, the selection of scale parameters has a great influence on the classification results, and the size and number of objects are different when different segmentation scales are used to segment the images. The smaller the scale is, the more objects will be generated, which will lead to over-segmentation. Conversely, the larger the scale is, the fewer objects will be generated, which will lead to insufficient segmentation. Because there are differences in spectral, texture and structure characteristics among different types of objects, there are different optimal segmentation scales for different types of objects. Based on the principle of high internal homogeneity and high external heterogeneity, the segmentation quality function is established in this paper. By comparing the value of segmentation quality function under different scale parameters, we can judge the optimal segmentation scale. 3. The research and improvement of the classification method based on multi-scale segmentation and decision tree are presented. In this paper, multi-scale segmentation and CART decision tree classification methods are deeply studied and analyzed. The concept of image segmentation is introduced in the traditional multi-scale segmentation method, and two methods are proposed: one is based on image "splicing lines" and the other is based on large-scale multi-scale segmentation. Multi-scale segmentation and decision tree classification for subblocks are carried out. Taking the 81 year sand lake image data as the experimental object, the accuracy of these two improved classification methods is compared with the results of traditional multi-scale segmentation and decision tree classification. The results show that the accuracy of multi-scale segmentation and decision tree classification based on block is obviously higher than that of multi-scale segmentation and decision tree classification of whole image. The method based on large scale multi-scale block has the highest precision of .4and the detection of lake transition area based on intelligent mobile terminal. This paper briefly introduces the Wuhan Water Culture App system, and realizes the detection of the transition region of the classified image map in the system transition function module.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751

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