Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …

Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …

Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …

The current hyperspectral image classification (HSIC) model based on the convolutional
neural network for feature extraction and softmax classifier has been prone to the barrier of …

Recently, many deep neural networks (DNN) have been proposed to solve the spectral
reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek …

Deep learning methods face significant challenges in practical cross-scene classification
tasks of hyperspectral images (HSIs), primarily due to the difficulty of acquiring labels and …

Multimodally distributed data is very common in remote sensing images, such as
hyperspectral images (HSIs). It is important to capture the local manifold structure while …

Cross-domain hyperspectral image classification is one of the major challenges in remote
sensing, especially for target domain data without labels. Recently, deep learning …