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融合CNN和MRF的激光点云层次化语义分割方法
激光点云 语义分割 层次化提取 残差学习 马尔可夫随机场(MRF)
2021/3/30
三维点云语义分割的结果包含着对场景中多个目标的识别，是三维场景信息提取的重要环节，在智慧城市等多个领域扮演关键角色。由于三维激光点云数据量庞大、场景复杂性高等问题，大多数现有方法只能以相对较低的识别率提取有限类型的对象。本文提出了一种在三维激光点云场景中结合残差学习和马尔可夫随机场（MRF）优化的层次化多类型目标自动提取框架。该框架首先将点云滤波为地面点和非地面点；然后从非地面点中提取建筑物以降低...
RESEARCH ON HIGH ACCURACY DETECTION OF RED TIDE HYPERSPECRRAL BASED ON DEEP LEARNING CNN
Red Tide CNN Hyperspectral Remote Sensing Glint
2018/5/14
Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts...
FUSING PANCHROMATIC AND SWIR BANDS BASED ON CNN – A PRELIMINARY STUDY OVER WORLDVIEW-3 DATASETS
Pan-sharpening, Convolutional Neural Network, Short-wave Infrared, Deep Learning, Image fusion, Remote Sensing
2018/5/14
The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpen...
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
Ship Detection Convolutional Neutral Networks (CNN) S-CNN Ship Model Construction
2016/11/23
Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various t...