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Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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Updated: Jul 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

城市场景细分模型基于多个尺度的混合特征.

Wenjuan Gu¹, Hongcheng Wang¹, Xiaobao Liu¹

¹Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming, 650500, China.

Mathematical biosciences and engineering : MBE

|July 28, 2023

概括

此摘要是机器生成的。

这项研究引入了一种新的多尺度特征混合模型,用于准确的城市场景细分. 这种先进的模型改善了土地类别的监测,以改善城市规划和资源管理.

关键词:

功能混合功能混合.多层次的注意力.遥感图像遥感图像遥感图像细分化细分化的细分化城市场景城市场景

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相关实验视频

Last Updated: Jul 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

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Published on: January 13, 2023

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

科学领域:

遥感遥感遥感遥感
城市规划城市规划
计算机视觉计算机视觉

背景情况:

有效的城市土地类别监测对于资源管理和规划至关重要.
城市遥感面临着不均的包裹分布,特征提取困难和信息丢失等挑战.

研究的目的:

提出一个多尺度的特征混合城市场景细分模型.
提高城市场景细分的准确性和完整性,以改善土地管理.

主要方法:

使用BlurPool的深度卷积编码器解码器网络来解决翻译不变性.
集成GSSConv和SE模块以改善信息交互和过冗余数据.
应用了多层次的注意力,以整合边界和全球上下文信息,以便更清晰地提取特征.

主要成果:

拟议的模型在BDCI2017数据集上实现了高性能.
获得的分数为83.1% (OA),71.0% (mIoU),82.7% (mRecall),82.7% (P) 和82.5% (Dice) 的分数.
在关键指标上表现优于几个已建立的细分网络.

结论:

多级特征混合模型显著提高了城市场景细分的准确性和完整性.
这一进步有助于理解城市发展,并为未来的规划策略提供信息.