Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Hyperspectral remote sensing in lithological map**, mineral exploration, and environmental geology: an updated review
S Peyghambari, Y Zhang - Journal of Applied Remote Sensing, 2021 - spiedigitallibrary.org
Hyperspectral imaging has been used in a variety of geological applications since its advent
in the 1970s. In the last few decades, different techniques have been developed by …
in the 1970s. In the last few decades, different techniques have been developed by …
Hyperspectral subspace identification
JM Bioucas-Dias… - IEEE Transactions on …, 2008 - ieeexplore.ieee.org
Signal subspace identification is a crucial first step in many hyperspectral processing
algorithms such as target detection, change detection, classification, and unmixing. The …
algorithms such as target detection, change detection, classification, and unmixing. The …
A review of nonlinear hyperspectral unmixing methods
In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …
variety of techniques based on this model has been proposed to obtain endmembers and …
SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery
As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …
(PCA) has been widely considered as an efficient and effective preprocessing step for …
Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks
Convolutional neural network (CNN) is well known for its capability of feature learning and
has made revolutionary achievements in many applications, such as scene recognition and …
has made revolutionary achievements in many applications, such as scene recognition and …
Self-supervised learning with adaptive distillation for hyperspectral image classification
Hyperspectral image (HSI) classification is an important topic in the community of remote
sensing, which has a wide range of applications in geoscience. Recently, deep learning …
sensing, which has a wide range of applications in geoscience. Recently, deep learning …