中文摘要：

基于地面的云分类由于在在不同大气的条件下面的云的外观的极端变化是挑战性的。质地分类技术最近被介绍了处理这个问题。一个新奇质地描述符，突出的本地二进制模式(SLBP ) ，为基于地面的云分类被建议。SLBP 利用最经常发生的模式(突出的模式) 捕获描述的信息。这个特征使 SLBP 柔韧到噪音。用基于地面的云图象的试验性的结果证明建议方法能比当前的最先进的方法完成更好的结果。

英文摘要：

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.