[HTML][HTML] Deep learning for urban land use category classification: A review and experimental assessment

Z Li, B Chen, S Wu, M Su, JM Chen, B Xu - Remote Sensing of …, 2024 - Elsevier
Map** the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …

Land use/land cover (LULC) classification using hyperspectral images: a review

C Lou, MAA Al-qaness, D AL-Alimi… - Geo-spatial …, 2024 - Taylor & Francis
In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …

Hyperspectral image classification using dictionary-based sparse representation

Y Chen, NM Nasrabadi, TD Tran - IEEE transactions on …, 2011 - ieeexplore.ieee.org
A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in
this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can …

Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest

C Debes, A Merentitis, R Heremans… - IEEE Journal of …, 2014 - ieeexplore.ieee.org
The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC)
of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic …

Hyperspectral image classification via kernel sparse representation

Y Chen, NM Nasrabadi, TD Tran - IEEE Transactions on …, 2012 - ieeexplore.ieee.org
In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is
proposed. Our approach relies on sparsely representing a test sample in terms of all of the …

Classifiers combination techniques: A comprehensive review

M Mohandes, M Deriche, SO Aliyu - IEEE Access, 2018 - ieeexplore.ieee.org
In critical applications, such as medical diagnosis, security related systems, and so on, the
cost or risk of action taking based on incorrect classification can be very high. Hence …

Hyperspectral image classification via multiple-feature-based adaptive sparse representation

L Fang, C Wang, S Li… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for
the classification of hyperspectral images (HSIs). The proposed method mainly includes the …

[HTML][HTML] Estimation of maize yield and flowering time using multi-temporal UAV-based hyperspectral data

J Fan, J Zhou, B Wang, N de Leon, SM Kaeppler… - Remote Sensing, 2022 - mdpi.com
Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of
continuous climate change and the reduced availability of arable land, it is urgent to breed …

Lithological map** from hyperspectral data by improved use of spectral angle mapper

X Zhang, P Li - International Journal of Applied Earth Observation and …, 2014 - Elsevier
The spectral angle mapper (SAM), as a spectral matching method, has been widely used in
lithological type identification and map** using hyperspectral data. The SAM quantifies …

Decision fusion of deep learning and shallow learning for marine oil spill detection

J Yang, Y Ma, Y Hu, Z Jiang, J Zhang, J Wan, Z Li - Remote Sensing, 2022 - mdpi.com
Marine oil spills are an emergency of great harm and have become a hot topic in marine
environmental monitoring research. Optical remote sensing is an important means to …