【摘要】：遙感圖像分類是人類獲取有效信息的重要手段,如何改善分類精度是遙感研究的重要內(nèi)容。傳統(tǒng)的圖像分類大多基于光譜特征,而對其空間結(jié)構(gòu)特征挖掘不足,造成分類結(jié)果不夠理想。圖像的紋理作為最具代表性的空間結(jié)構(gòu)特征,在改善圖像分類精度方面有其獨特的優(yōu)勢和巨大的應(yīng)用價值。紋理分析方法眾多,傳統(tǒng)的統(tǒng)計分析方法成熟穩(wěn)定,有著廣泛的應(yīng)用基礎(chǔ),新興的模型分析方法發(fā)展迅速,特別是分形模型在紋理分析中的應(yīng)用受到極大的關(guān)注。 本文選取福州市區(qū)中南部城鄉(xiāng)交界處的一塊矩形區(qū)域作為典型試驗區(qū),利用傳統(tǒng)的灰度共生矩陣模型提取典型試驗區(qū)圖像的4個紋理特征,利用新興的分形模型提取典型試驗區(qū)圖像的1個紋理特征。在此基礎(chǔ)上,將1個分形紋理、4個灰度共生矩陣紋理與8個光譜特征等13個特征變量組合,構(gòu)建多源特征數(shù)據(jù)庫,開展輔以紋理的典型試驗區(qū)圖像監(jiān)督分類和非監(jiān)督分類實驗。實驗表明,在圖像分類中加入紋理特征能夠彌補光譜特征的不足,有效的改善圖像的分類精度；通常圖像的分類精度會隨著多源特征變量的增多而進一步提高；不同的紋理特征對分類精度的影響程度不同,與傳統(tǒng)的灰度共生矩陣紋理相比,新興的分形紋理在改進分類精度上效率更高,效果更好。 最后,將研究成果用于福州市區(qū)四個時相的遙感圖像分類,驗證了輔以紋理的遙感圖像分類的普適性,通過統(tǒng)計分類結(jié)果,分析了近14年福州市區(qū)土地利用／覆被變化的情況和原因。
[Abstract]:Remote sensing image classification is an important means for human to obtain effective information. How to improve the classification accuracy is an important content of remote sensing research. The traditional image classification is mostly based on spectral features, but the spatial structure features are not well mined, so the classification results are not satisfactory. As the most representative spatial structure feature, image texture has its unique advantages and great application value in improving image classification accuracy. There are many texture analysis methods, traditional statistical analysis methods are mature and stable, and have a wide application foundation. The new model analysis methods have developed rapidly, especially the application of fractal model in texture analysis has received great attention. In this paper, a rectangular area at the junction of urban and rural areas in the central and southern part of Fuzhou is selected as the typical experimental area, and the four texture features of the typical experimental area image are extracted by using the traditional gray level co-occurrence matrix model. A new fractal model is used to extract a texture feature from a typical experimental area image. On this basis, 13 feature variables, such as 1 fractal texture, 4 gray-scale co-occurrence matrix texture and 8 spectral features, are combined to construct a multi-source feature database and to carry out image supervised classification and unsupervised classification experiments in a typical experimental area supplemented by texture. Experiments show that adding texture features to image classification can make up for the deficiency of spectral features, and improve the classification accuracy of images effectively, usually the classification accuracy of images will be further improved with the increase of multi-source feature variables. The effect of different texture features on classification accuracy is different. Compared with the traditional gray-level co-occurrence matrix texture, the new fractal texture is more efficient and better in improving classification accuracy. Finally, the research results are applied to the classification of four temporal remote sensing images in Fuzhou urban area, and the universality of remote sensing image classification with texture is verified. This paper analyzes the situation and causes of land use / cover change in Fuzhou urban area in recent 14 years.
【學(xué)位授予單位】：福建師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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