Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

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 …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Geosciences is a field of great societal relevance that requires solutions to several urgent
problems facing our humanity and the planet. As geosciences enters the era of big data …

Inspired by recent development of artificial satellite, remote sensing images have attracted
extensive attention. Recently, notable progress has been made in scene classification and …

Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …

Hyperspectral imaging is a widely used technique in remote sensing in which an imaging
spectrometer collects hundreds of images (at different wavelength channels) for the same …