Segmentation and classification are two imperative, yet challenging tasks in image analysis
for remote-sensing applications. In the former, an image is divided into spatially continuous …

In this paper, we propose a new framework for spectral-spatial classification of hyperspectral
image data. The proposed approach serves as an engine in the context of which active …

This paper proposes a novel change-detection-driven transfer learning (TL) approach to
update land-cover maps by classifying remote-sensing images acquired on the same area …

Active learning, which has a strong impact on processing data prior to the classification
phase, is an active research area within the machine learning community, and is now being …

Less training samples are a challenging problem in hyperspectral image classification.
Active learning and semisupervised learning are two promising techniques to address the …

The validity of training samples collected in field campaigns is crucial for the success of land
use classification models. However, such samples often suffer from a sample selection bias …

This paper aims at analyzing and comparing active learning (AL) and semisupervised
learning (SSL) methods for the classification of remote sensing (RS) images. We present a …

随着模式识别, 机器学**, 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 …

This paper presents a novel technique for addressing domain adaptation (DA) problems
with active learning (AL) in the classification of remote sensing images. DA models the …

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification.
However, RF cannot leverage its full potential in the case of limited labeled samples. To …