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相关概念视频

Survival Tree01:19

Survival Tree

118
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
118

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代训练样本增强用于提高土地覆盖面变化检测性能,使用深度学习神经网络.

Zhiyong Lv, Haitao Huang, Weiwei Sun

    IEEE transactions on neural networks and learning systems
    |June 21, 2023
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    概括

    一种代训练样本增强 (ITSA) 策略增强了使用遥感图像进行土地覆盖变化检测 (LCCD) 的深度学习. 这种方法减少了手工标签工作,提高了检测准确度,提供了强大而可适应的解决方案.

    科学领域:

    • 遥感 遥感 遥感 遥感
    • 地理空间分析是什么
    • 人工智能的人工智能

    背景情况:

    • 使用遥感图像进行准确的土地覆盖变化检测 (LCCD) 是至关重要的.
    • 深度学习模型需要广泛的标记数据来进行有效的训练.
    • 对于比特时空遥感图像的手动样品标签是繁的,耗时的,需要专业知识.

    研究的目的:

    • 提出一个代训练样本增强 (ITSA) 策略,以通过深度学习提高LCCD性能.
    • 为了减少对LCCD任务中手动样品标签的依赖.
    • 提高基于深度学习的LCCD的效率和准确性.

    主要方法:

    • 开发了一种代训练样本增强 (ITSA) 策略.
    • 测量样本与邻近块的相似性,以识别潜在的新样本.
    • 用增强样本和预测结果反复训练一个神经网络.
    • 将ITSA与LCCD的现有深度学习网络结合起来.

    主要成果:

    • 当与深度学习网络集成时,拟议的ITSA战略有效地提高了LCCD性能.
    • 实验表明检测准确度有显著改善,与最先进的方法相比,总体准确度增加范围从0.38%到7.53%.

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  • ITSA战略在各种图像类型和深度学习架构中显示出强大,通用和普遍适应性性能.

    • 结论:

    • ITSA战略是增强基于深度学习的LCCD的一个有价值的方法.
    • 它有效地解决了手动样品标签的挑战,减少了努力和时间.
    • 该方法在LCCD精度上提供了显著而可靠的改进,适用于各种遥感场景.