Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of
genomics and environment on plants, limiting the progress of smart breeding and precise …

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used
to construct the detection models. However, the existing studies only consider the multiscale …

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …

Current hyperspectral image classification assumes that a predefined classification system
is closed and complete, and there are no unknown or novel classes in the unseen data …

Taking into account the limitations of optical imaging, image acquisition equipment is usually
designed to make a trade-off between spatial information and spectral information …

Masked image modeling (MIM) is a highly popular and effective self-supervised learning
method for image understanding. The existing MIM-based methods mostly focus on spatial …

Scene classification is an indispensable part of remote sensing image interpretation, and
various convolutional neural network (CNN)-based methods have been explored to improve …