公开(公告)号：US11615615B2

公开(公告)日：2023-03-28

申请号：US17153967

申请日：2021-01-21

Applicant: SOUTHEAST UNIVERSITY

Inventor： Zhe Li ,  Yukun He ,  Yuning Cheng ,  Xiang Zhou ,  Kaiyu Zhao ,  Xiao Han ,  Feifei Chen ,  Shuang Song ,  Xinyi Lu ,  Xiaoshan Lin

IPC: G06V20/10 ,  G06T7/174

Abstract: The present invention discloses a method and an apparatus for extracting mountain landscape buildings based on high-resolution remote sensing images. The method comprises: segmenting a remote sensing image, and extracting non-vegetation areas from the remote sensing image by using NDVI; segmenting the non-vegetation areas, and extracting building areas by using NDBI; segmenting the building areas again, and calculating a normalized difference build shadow index NSBI of each patch; calculating NSBI separator of each patch in the non-vegetation areas and setting a separator threshold, and extracting landscape building areas based on the threshold. In the present invention, by introducing a near infrared band in the remote sensing image spectrum, in which there is a significant difference between shadows and non-shadows, the influence of large shadow areas in mountainous shady areas in the remote sensing image on the result of extraction is reduced.

公开(公告)号：US12048549B1

公开(公告)日：2024-07-30

申请号：US18565501

申请日：2023-04-04

Applicant: SOUTHEAST UNIVERSITY

Inventor： Zhe Li ,  Liya Wang ,  Xiao Han ,  Jie Li ,  Qixin Zhang ,  Mingjing Dong ,  Mingchen Xu ,  Shuang Wu ,  Yi Shi ,  Haini Chen ,  Qiaochu Wang

IPC: G06V10/00 ,  A61B5/0205 ,  A61B5/0533 ,  A61B5/352 ,  A61B5/378 ,  A61B5/397 ,  G06N20/00 ,  G06V10/26 ,  G06V10/764 ,  G06V20/00 ,  G06Q50/26

CPC classification number: A61B5/378 ,  A61B5/0205 ,  A61B5/0533 ,  A61B5/352 ,  A61B5/397 ,  G06N20/00 ,  G06V10/26 ,  G06V10/764 ,  G06V20/39 ,  A61B2503/12 ,  G06Q50/26

Abstract: A street greening quality detection method based on physiological activation recognition is provided. The street greening quality detection method includes establishing a greening quality factor index system, and obtaining and uniformly processing street greening images; collecting raw data, and performing reclassification and differential wave processing on the raw data to obtain valid physiological data that can be used for activation feature recognition of greening quality factors; calculating physiological activation feature parameters, training the physiological activation feature parameters by transfer learning fusion to determine importance of physiological activation features, and recognizing weighted average greening activation indexes of the greening quality factors; analyzing weighted average greening activation index data of the greening quality factors to form a street greening quality detection model; and inputting annotated street samples to be analyzed into the street greening quality detection model to obtain annotated results of street greening quality grading detection target data.