CAJ | 학술논문

月海和月陆是两种最主要的月貌单元，对于月海及月陆快速准确地识别是进行各项月球研究的重要基础。目前，月海和月陆的识别大多采用DEM结合其派生地形因子建立指标体系的方法。这种方法虽然可在宏观尺度对月海和月陆进行识别和提取，但仍存在2个问题：（1）可扩展性差，不同地区难以共用同一套地形因子构建指标体系；（2）指标体系中各因子权重设置具有较大的主观性。针对以上问题，本文以“嫦娥一号”探测器获取的全月球DEM数据，从月表地形纹理特征的角度出发，提出一种以月表DEM数据识别月海、月陆的自动快速的方法。首先，利用灰度共生矩阵模型，以DEM数据为基础，实现对典型月海、月陆地形纹理特征的量化，然后，对量化指标的筛选，构建能有效区分两类月表形貌单元的特征向量。在此基础上，选用离差平方和作为识别器，最终实现对月海和月陆的自动识别。本文识别方法的整体识别率达到85.7%；综上可知，该方法既能克服原有方法中因子权重设置的主观性，又具有较好的通用性。
월해화월륙시량충최주요적월모단원，대우월해급월륙쾌속준학지식별시진행각항월구연구적중요기출。목전，월해화월륙적식별대다채용DEM결합기파생지형인자건립지표체계적방법。저충방법수연가재굉관척도대월해화월륙진행식별화제취，단잉존재2개문제：（1）가확전성차，불동지구난이공용동일투지형인자구건지표체계；（2）지표체계중각인자권중설치구유교대적주관성。침대이상문제，본문이“항아일호”탐측기획취적전월구DEM수거，종월표지형문리특정적각도출발，제출일충이월표DEM수거식별월해、월륙적자동쾌속적방법。수선，이용회도공생구진모형，이DEM수거위기출，실현대전형월해、월륙지형문리특정적양화，연후，대양화지표적사선，구건능유효구분량류월표형모단원적특정향량。재차기출상，선용리차평방화작위식별기，최종실현대월해화월륙적자동식별。본문식별방법적정체식별솔체도85.7%；종상가지，해방법기능극복원유방법중인자권중설치적주관성，우구유교호적통용성。
The mare and lunar highland are two major types of lunar morphology. The rapid and reliable identifi-cation of these two kinds of lunar morphology is an important basis in lunar research. Currently, major methods for identifying the mare and highland are based on the integrated evaluation index system, which is usually com-bined with the land surface parameters derived from DEM. Although the mare and highland can be identified by this method, it contains two problems yet. One is the lack of extensibility, because it is difficult for different re-gions of lunar to share one index system based on the same terrain factors. The other is the significant subjectivi-ty in weight setting for each factor in the index system. To overcome the problems mentioned above, a new meth-od considering the terrain texture features from lunar DEM is proposed by using the 500 m lunar DEM, which is produced from the global moon data obtained by Chinese satellite Chang’E-1(CE-1).Six typical mare sample ar-eas and six typical highland sample areas were selected as the training zones. To construct the different terrain texture eigenvectors between the mare and highland, principal component analysis (PCA) was used to extract the main composition factors after the execution of quantitative analysis based on Gray level co-occurrence matrix (GLCM) model. Then the area located on 40° E~120° W, 0°~30° S was selected as the test area and the same ap-proach in constructing terrain texture eigenvectors was used in this area. At last, supervised classification method was taken to identify those two types of lunar morphology. The recognition rate was about 85.7%. According to the comparative results between the new method and the traditional manual visual interpretation with Chang’E-1 (CE-1) remote sensing image (in 120 m resolution),the proposed method is more effective and precise in identify-ing the mare and highland. Meanwhile, this method is driven by objective data, which spontaneously overcomes the subjectivity deficiency of current methods. Furthermore, this research provides a new thinking strategy of identifying and extracting different geomorphology based on the texture features from DEMs.