Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

随着模式识别, 机器学习, 遥感技术等相关学科领域的发展, 高光谱遥感影像分类研究取得快速
进展. 本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展, 在总结分类策略的基础 …

Deep learning methods have recently been successfully explored for hyperspectral image
(HSI) classification. However, training a deep-learning classifier notoriously requires …

In this article, a comprehensive review of the state-of-art graph-based learning methods for
classification of the hyperspectral images (HSI) is provided, including a spectral information …

Geographic object segmentation is a fundamental yet challenging problem for remote
sensing image interpretation. The prevalent paradigm to solve this problem is to train deep …

In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …

In the process of semi-supervised hyperspectral image classification, spatial neighborhood
information of training samples is widely applied to solve the small sample size problem …

For both traditional classification and current popular deep learning methods, the limited
sample classification problem is very challenging, and the lack of samples is an important …

Change detection is an important technique providing insights to urban planning, resources
monitoring, and environmental studies. For multispectral images, most semi-supervised …