Using spectrum differentiation and combination for target detection of minerals

Dehnavi, Sahar; Maghsoudi, Yasser; Valadanzoej, M. J.

doi:10.1016/j.jag.2016.10.005

Cited by 6 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CEM constrains a desired target signature by using a specific gain. The idea of this algorithm arises from MVDR (minimum variance distortion-less response) in array processing, with the desired target signature interpreted as the direction of the arrival from a desired signal [10,[63][64][65]. Coming back to the remote sensing-based target detection problem, the array of sensors can be interpreted as a bank of spectral channels used in a remote sensing instrument and the desired direction of the signal's arrival as the vector direction of the desired target signature.…”

Section: Constrained Energy Minimization (Cem)mentioning

confidence: 99%

See 1 more Smart Citation

Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

et al. 2020

Self Cite

View full text Add to dashboard Cite

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

show abstract

Section: Constrained Energy Minimization (Cem)mentioning

confidence: 99%

“…Applying a set of filter gains, the concept can be used to constrain a set of multiple targets [9]. A better and more detailed introduction to this method is available in different references, like [10,[63][64][65].…”

Section: Constrained Energy Minimization (Cem)mentioning

confidence: 99%

Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The size of the anomalous objects can also be in sub-pixel scale. Target and anomaly detection are widely used for reconnaissance and surveillance, and also in other areas like detection of special species in agriculture and rare mineral in geology [50,83,206].…”

Section: Target/anomaly Detectionmentioning

confidence: 99%

Machine learning based hyperspectral image analysis: A survey

Gewali,

Monteiro,

Saber

2018

Preprint

View full text Add to dashboard Cite

Hyperspectral sensors enable the study of the chemical and physical properties of scene materials remotely for the purpose of identification, detection, chemical composition analysis, and physical parameter estimation of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper surveys and compares recent machine learning-based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target/anomaly detection, unmixing, and physical/chemical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.

show abstract

Quantitative analysis of the oil mixture using PLS combined with spectroscopy detection

Zhang

et al. 2021

Optik

View full text Add to dashboard Cite

Using spectrum differentiation and combination for target detection of minerals

Cited by 6 publications

References 32 publications

Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

Machine learning based hyperspectral image analysis: A survey

Quantitative analysis of the oil mixture using PLS combined with spectroscopy detection

Contact Info

Product

Resources

About