基于DEM紋理特征的月貌自動識別方法探究
發(fā)布時間：2019-03-25 19:02
【摘要】：月海和月陸是兩種最主要的月貌單元,對于月海及月陸快速準(zhǔn)確地識別是進(jìn)行各項月球研究的重要基礎(chǔ)。目前,月海和月陸的識別大多采用DEM結(jié)合其派生地形因子建立指標(biāo)體系的方法。這種方法雖然可在宏觀尺度對月海和月陸進(jìn)行識別和提取,但仍存在2個問題:(1)可擴(kuò)展性差,不同地區(qū)難以共用同一套地形因子構(gòu)建指標(biāo)體系;(2)指標(biāo)體系中各因子權(quán)重設(shè)置具有較大的主觀性。針對以上問題,本文以"嫦娥一號"探測器獲取的全月球DEM數(shù)據(jù),從月表地形紋理特征的角度出發(fā),提出一種以月表DEM數(shù)據(jù)識別月海、月陸的自動快速的方法。首先,利用灰度共生矩陣模型,以DEM數(shù)據(jù)為基礎(chǔ),實現(xiàn)對典型月海、月陸地形紋理特征的量化,然后,對量化指標(biāo)的篩選,構(gòu)建能有效區(qū)分兩類月表形貌單元的特征向量。在此基礎(chǔ)上,選用離差平方和作為識別器,最終實現(xiàn)對月海和月陸的自動識別。本文識別方法的整體識別率達(dá)到85.7%;綜上可知,該方法既能克服原有方法中因子權(quán)重設(shè)置的主觀性,又具有較好的通用性。
[Abstract]:Lunar sea and lunar land are the two most important lunar feature units. The fast and accurate recognition of lunar sea and lunar land is an important basis for all kinds of lunar studies. At present, the identification of lunar sea and lunar land mostly uses DEM combined with its derived terrain factors to establish the index system. Although this method can be used to identify and extract lunar sea and lunar land on a macro scale, there are still two problems: (1) poor scalability, it is difficult to share the same terrain factors in different areas to construct an index system; (2) the weight setting of each factor in the index system is subjective. In order to solve the above problems, this paper presents an automatic and rapid method to identify the moon sea and lunar land with the lunar surface DEM data from the point of view of the topography texture features of the moon surface, based on the all-moon DEM data obtained by Chang'e-1 spacecraft. Firstly, the gray-level co-occurrence matrix model is used to quantify the texture features of the typical lunar sea and land topography based on the DEM data. Then, the feature vectors which can effectively distinguish the two types of monthly surface topography units are constructed by screening the quantized indexes. On this basis, the sum of squared deviation is selected as the discriminator to realize the automatic recognition of lunar sea and lunar land. The overall recognition rate of this method is up to 85.7%, which shows that the method can not only overcome the subjectivity of factor weight setting in the original method, but also has better universality.
【作者單位】： 南京師范大學(xué)地理科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金項目(41171320) 江蘇省高校自然科學(xué)基金重大項目(13KJA170001) 江蘇省研究生科研創(chuàng)新計劃項目(KYLX_0701)
【分類號】：P184;P208
[Abstract]:Lunar sea and lunar land are the two most important lunar feature units. The fast and accurate recognition of lunar sea and lunar land is an important basis for all kinds of lunar studies. At present, the identification of lunar sea and lunar land mostly uses DEM combined with its derived terrain factors to establish the index system. Although this method can be used to identify and extract lunar sea and lunar land on a macro scale, there are still two problems: (1) poor scalability, it is difficult to share the same terrain factors in different areas to construct an index system; (2) the weight setting of each factor in the index system is subjective. In order to solve the above problems, this paper presents an automatic and rapid method to identify the moon sea and lunar land with the lunar surface DEM data from the point of view of the topography texture features of the moon surface, based on the all-moon DEM data obtained by Chang'e-1 spacecraft. Firstly, the gray-level co-occurrence matrix model is used to quantify the texture features of the typical lunar sea and land topography based on the DEM data. Then, the feature vectors which can effectively distinguish the two types of monthly surface topography units are constructed by screening the quantized indexes. On this basis, the sum of squared deviation is selected as the discriminator to realize the automatic recognition of lunar sea and lunar land. The overall recognition rate of this method is up to 85.7%, which shows that the method can not only overcome the subjectivity of factor weight setting in the original method, but also has better universality.
【作者單位】： 南京師范大學(xué)地理科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金項目(41171320) 江蘇省高校自然科學(xué)基金重大項目(13KJA170001) 江蘇省研究生科研創(chuàng)新計劃項目(KYLX_0701)
【分類號】：P184;P208
【參考文獻(xiàn)】
相關(guān)期刊論文 前9條
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