- Xu Yongyang, Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens. 2018, 10, 144.
以前的研究没有能够清晰的检测出物体的边缘，并且缺少消除椒盐噪声的能力，具有相似光谱值的像元经常被错分 (These methods cannot clearly detect the boundary of the objects, and lack the ability to remove the salt-and pepper class noise; some pixels with similar spectral values are usually mis-classify)
- ZhaoWenzhi. Object-Based Convolutional Neural Network for High-Resolution Imagery Classification. JSTAEORS, 2017.
为了解决在像素层面难以精确区分不同物体的边缘这个问题，作者提出了一种基于面向对象的深度学习框架。结果表明可以很好的区分居民地和商业用地。( To solve the problem that due to the hierarchical abstract nature of deep learning methods, it is difficult to capture the precise outline of different objects at the pixel level
- Zhigang Cao, Ronghua Ma,Effects of broad bandwidth on the remote sensing of inland waters: Implications for high spatial resolution satellite data applications, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 153, 2019,Pages 110-122 .
Understanding the effects of bandwidth on the optical properties of the water and finding a balance between spatial resolution and bandwidth to determine the sensor’s bandwidth requirement for the high-resolution optical remote sensing of inland waters
- 高分影像的实时精确解译有助于城市规划和灾难援救(Timely and accurate classificationand interpretation of high-resolution images are very important for urban planningand disaster rescue ).
2019.5.22“High resolution remote sensing”+ urban + class* = 152 (被引频次：降序33/26)