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一种基于深度反卷积神经网络的语义分类方法,用于土地覆盖的遥感图像.
Ming Wang1, Anqi She2, Hao Chang3
1Network Information Center, Jilin Normal University, Siping, 136000, China.
Scientific reports
|March 28, 2024
概括
一种新的深度解卷神经网络方法准确地分类不平衡的土地覆盖远程传感图像. 这种方法提高了少数群体类别的认可,提高了整体语义分类的准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在遥感中,土地覆盖的分类受到不平衡的数据的挑战,其中一些类别是罕见的,导致少数群体类别的认可不足.
- 由于数据不平衡,现有的方法难以对土地覆盖的遥感图像进行多目标语义分类.
研究的目的:
- 为土地覆盖的遥感图像提出一个语义分类方法,以解决数据不平衡的问题.
- 为了提高土地覆盖远程传感图像的多目标语义分类的准确性.
主要方法:
- 一个深度解卷神经网络被用于对土地覆盖的遥感图像进行语义细分.
- 一个改进的顺序聚类算法提取了四个语义特征:颜色,纹理,形状和大小.
- 使用随机森林算法来分类和识别这些提取的语义特征.
主要成果:
- 拟议的方法在分类多目标语义类型的土地覆盖远程探测图像方面取得了高准确性.
- 平均子相似系数达到0.9877,表明精确的细分.
- 平均豪斯多夫距离为0.9911,证实了准确的边界划定.
结论:
- 开发的深度解卷神经网络方法有效地克服了土地覆盖数据不平衡的挑战.
- 这种方法显著提高了土地覆盖的远程传感图像的语义分类准确性,特别是对于少数群体的类别.
相关概念视频
01:26Classification of Systems-I
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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