colloquia, winter term colloquia series, University of Salzburg, Austria
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.
Talk, ISPRS Workshop, Changsha, Hunan, China
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.
Presentation, 2019中国地理信息科学理论与方法学术年会, 上海, 中国
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。
Poster, International Geoscience and Remote Sensing Symposium, Yokohama, Japan
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.