Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data
The enormous increase of remote sensing data from airborne and space-borne platforms, as
well as ground measurements has directed the attention of scientists towards new and …
well as ground measurements has directed the attention of scientists towards new and …
Big data and machine learning with hyperspectral information in agriculture
KLM Ang, JKP Seng - IEEE Access, 2021 - ieeexplore.ieee.org
Hyperspectral and multispectral information processing systems and technologies have
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses
This study proposes a novel learning scheme for the kernel extreme learning machine
(KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed …
(KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed …
Unmanned Aerial System (UAS)-based phenoty** of soybean using multi-sensor data fusion and extreme learning machine
Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is
imperative for high-throughput phenoty** in precision agriculture. Although fusion of data …
imperative for high-throughput phenoty** in precision agriculture. Although fusion of data …
Local binary patterns and extreme learning machine for hyperspectral imagery classification
It is of great interest in exploiting texture information for classification of hyperspectral
imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich …
imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich …
Data-driven decision making in precision agriculture: The rise of big data in agricultural systems
In this paper, we provide a review of the research dedicated to applications of data science
techniques, and especially machine learning techniques, in relevant agricultural systems …
techniques, and especially machine learning techniques, in relevant agricultural systems …
Hyperspectral image reconstruction by deep convolutional neural network for classification
Spatial features of hyperspectral imagery (HSI) have gained an increasing attention in the
latest years. Considering deep convolutional neural network (CNN) can extract a hierarchy …
latest years. Considering deep convolutional neural network (CNN) can extract a hierarchy …
Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine
Extreme learning machine (ELM) is a single-layer feedforward neural network based
classifier that has attracted significant attention in computer vision and pattern recognition …
classifier that has attracted significant attention in computer vision and pattern recognition …
Multi-view learning for hyperspectral image classification: An overview
Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …
object's information in hundreds of spectral bands. However, how to make full advantage of …
An efficient machine learning approach for diagnosis of paraquat-poisoned patients
L Hu, G Hong, J Ma, X Wang, H Chen - Computers in Biology and Medicine, 2015 - Elsevier
Numerous people die of paraquat (PQ) poisoning because they were not diagnosed and
treated promptly at an early stage. Till now, determination of PQ levels in blood or urine is …
treated promptly at an early stage. Till now, determination of PQ levels in blood or urine is …