CAJ | 학술논문

随着高分辨率遥感影像在矿山分类工作中的广泛应用，面向对象的多尺度分割技术成为研究热点。最优尺度的选取是此类技术的关键。为获取高分遥感影像最适宜的分割尺度，本文以赣州稀土矿山高分辨率遥感影像为源数据，选择与邻域绝对均值差分方差比方法RMAS（ Ratio of Mean Difference to Neighbors （ ABS） to Stand-ard Deviation ）方法获取最优分割尺度。通过分割质量值对分割结果进行评价分析，研究验证了RMAS方法的可行性。
수착고분변솔요감영상재광산분류공작중적엄범응용，면향대상적다척도분할기술성위연구열점。최우척도적선취시차류기술적관건。위획취고분요감영상최괄의적분할척도，본문이공주희토광산고분변솔요감영상위원수거，선택여린역절대균치차분방차비방법RMAS（ Ratio of Mean Difference to Neighbors （ ABS） to Stand-ard Deviation ）방법획취최우분할척도。통과분할질량치대분할결과진행평개분석，연구험증료RMAS방법적가행성。
Sincehigh resolution remote sensing image is widely used in the work of mining classification , the technologies of object -oriented multi-scale segmentation become a hot topic .The selection of the optimal scale is the key to such technology .In order to obtain the optimal segmentation scale for high resolution sensing image , high-resolution remote sensing images of Ganzhou rare earth mines are selected as source data and RMAS ( Ratio of Mean Difference to Neighbors ( ABS) to Standard Deviation ) method to obtain the optimal scale in the paper .Segmentation results are evaluated by segmentation quality value , and the study proves the feasibility of RMAS method.