Dr. Wenzhi Zhao

Dr. Wenzhi Zhao

Lecturer at Beijing Normal University, expert in Big Earth-Obervation data mining

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

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portfolio

publications

Learning multiscale and deep representations for classifying remotely sensed imagery

Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2016

This paper is about learning multiscale feature for remote sensing image classification.

Recommended citation: Wenzhi Zhao, et al. (2016). "Paper Learning multiscale and deep representations for classifying remotely sensed imagery." ISPRS Journal of Photogrammetry and Remote Sensing. 113. https://www.sciencedirect.com/science/article/pii/S0924271616000137

Object-based convolutional neural network for high-resolution imagery classification

Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017

This paper is about sample enrichment with hyperspectral image classification.

Recommended citation: Wenzhi Zhao, et al. (2017). "Object-based convolutional neural network for high-resolution imagery classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(7). https://ieeexplore.ieee.org/abstract/document/7890382/

Exploring semantic elements for urban scene recognition Deep integration of high-resolution imagery and OpenStreetMap

Published in ISPRS journal of photogrammetry and remote sensing, 2019

This paper is about deeply intergrate OSM and high-resolution imagery.

Recommended citation: Wenzhi Zhao, et al. (2019). "Exploring semantic elements for urban scene recognition Deep integration of high-resolution imagery and OpenStreetMap." ISPRS journal of photogrammetry and remote sensing. 151. https://www.sciencedirect.com/science/article/pii/S0924271619300887

Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net

Published in IEEE Geoscience and Remote Sensing Letters, 2019

This paper is about sample enrichment with hyperspectral image classification.

Recommended citation: Wenzhi Zhao, et al. (2019). "Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net." IEEE Geoscience and Remote Sensing Letters. 17(3). https://ieeexplore.ieee.org/abstract/document/8754768/

Published in , 1900

talks

An object-based convolutional neural network for urban building semantic classification

Published:

Building classification (including identifying geometrical shapes and their semantic labels) in aerial and satellite images is one of the most important issues in urban planning, environment management, and disaster recovery. With the fast development of remote sensing technology, both the spectral and spatial resolution of remote sensing image have been significantly improved. In this regard, remote sensing imagery may contain hundreds of spectral bands whilst the spatial resolution can be as high as sub-meter for each pixel. The difficulties of building identification have significantly increased as the quickly evolving spatial and spectral variations. Moreover, due to the limitation of remotely sensed data, it is almost impossible to acquire the semantic information of ground-based buildings.

基于众源地理数据与深度学习的城市建筑功能信息提取(in Chinese)

Published:

随着深度学习技术的普及，遥感影像，特别是高分辨率遥感影像已经能够实现精细化的自动解译。然而，遥感影像分类结果仍以地表覆盖（land cover）类型为主，而在实际应用中地表覆盖的功能类型（semantic labels）更具有利用价值。为此，本报告系统梳理了当前主流的深度学习与遥感影像分类工作，并引入了众源地理数据结合地表覆盖类型数据，实现了地物功能类型信息获取。

Generative Adversarial Network for Fine-scale change detection

Published:

Change detection by comparing two bitemporal images is one of the most fundamental challenges for dynamic monitoring of the Earth surface. To achieve this purpose, a seasonal invariant term is introduced to maximally suppress pseudochanges, whereas the MeGAN explores the transition patterns between adjacent images in a self-learning fashion.

Semantic Classification of Urban Buildings using deep learning and VGI information

Published:

Semantically classification of urban buildings is crucial for disaster evacuation and city management. In this presentation, the deep learning strategy for remote sensing image interpretation was straightened out. In addition, the OpenStreetMap (OSM) data was also included for semantic enrichment of the urban area classification map.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

时空大数据与社会感知(in Chinese)

Workshop, 地理科学学部，北京师范大学, 2019

课程简介

大数据时代产生了大量具有时空标记、能够描述个体行为的时空大数据，如卫星遥感数据、手机数据、出租车数据、社交媒体数据等。这些数据为人们进一步定量化感知空间环境、人文信息及社会经济状况的时空分布格局提供了一种新的手段。 本课程从大数据背景下时空信息处理、提取与应用的需求出发，普及时空大数据特征、社会感知的基本概念、原理以及在行业领域中的应用，培养学生从大数据、机器学习的角度处理、分析和提取时空地理信息。课程内容将涵盖但不局限于：空间信息的基本概念和原理、空间大数据模式发现、行业应用和社会感知中的应用。授课方式将采用讲课、展示（图像、软件系统）、组织课堂讨论、上机操作、参观等方式。学生通过听课、阅读相关资料、上机实习等方式理解和掌握相关内容。