[PDF] Top 20 Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
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Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
... over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture and chemical ...including remote sensing [1]. HSI data ... See full document
9
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
... as Hyperspectral Imaging ...in remote sensing applications, quantitative results are generated for objective ...whole feature sets are ... See full document
22
Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... To address these drawbacks, we propose the extended 2-D version of SSA (2D-SSA) to fully explore the spatial correlation of HSI images. Based on the well-known SVD theory, the 2D-SSA aims to provide a systematic solution ... See full document
32
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
... As part of the DL framework being explored in the very recent years, SAEs are proved to be an effective method for feature extraction/abstraction and data reduction in HSI. In this ... See full document
18
Singular spectrum analysis for effective feature extraction in hyperspectral imaging
... other hand, the EMD technique is proved to decrease the accuracy, as already stated in [8]. For the Salinas C dataset, the SSA technique also improves the performance, although the classification accuracy of the Baseline ... See full document
5
Joint bilateral filtering and spectral similarity-based sparse representation : a generic framework for effective feature extraction and data classification in hyperspectral imaging
... range, hyperspectral imaging (HSI) has been an important surveillance and reconnaissance technology for military [1] as well as other civil applications such as food quality assessment [2, 3], ... See full document
22
Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent technique for generic ... See full document
31
Data Fusion for Urban Feature Extraction from LiDAR and Hyperspectral Data
... the remote sensing imaging technology, application of decision fusion approaches for fusion of images from different sensors is becoming more and more widespread in remote ...single ... See full document
6
Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging
... 100, data processing on the hypercube appears like video analytics, a really time-consuming and memory-intensive ...sional data results in the curse-of-dimensionality is- sue, also known as the Hughes ... See full document
10
Analysis of forest areas by advanced remote sensing systems based on hyperspectral and LIDAR data
... Remote sensing hyperspectral sensors are important and powerful instruments for addressing classifica- tion problems in complex forest scenarios, as they allow one a detailed characterization of the ... See full document
110
Hyperspectral Remote Sensing for Terrestrial Applications
... for data mining to eliminate redun dant bands are discussed. Various data mining methods are ...of hyperspectral analysis are presented and ...include feature extraction meth ods and ... See full document
34
Hyperspectral Image Classification for Remote Sensing
... spectral-spatial feature extraction techniques could successfully deliver a representation of the two kinds of information existing in the hy- perspectral datasets, they are extremely ...automatic ... See full document
142
Utilizing hyperspectral remote sensing for soil gradation
... Hyperspectral remote sensing has the capability of determining attributes such as clay and sand content of soil within the laboratory and ...field data collection ...processing ... See full document
14
Spectral Imaging for Remote Sensing
... tral imaging. Physics-based tech- niques and automated feature- extraction approaches associated with hyperspectral sensor data give more information to charac- terize these complex ... See full document
26
Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data For Improvement of Land Cover Classification
... of remote sensing data from multiple sensors has been remarkably increased for classification ...of hyperspectral and Light Detection And Ranging (LIDAR) data in classification ...on ... See full document
6
Automatic extraction of faults and fractal analysis from remote sensing data
... radar data are acquired in as- cending and descending orbits, allowing to measure precisely faults otherwise in shadow on optic ...side-looking imaging radar, the same scene imaged in opposite directions ... See full document
8
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
... In remote sensing, hyperspectral image classification addresses the problem of land- cover class identification and thematic map generation, which has extensive applica- tions in precision ... See full document
179
A new kernel method for hyperspectral image feature extraction
... for remote discrimination of subtle differences in ground ...of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image ...processing. ... See full document
10
Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... for feature extraction, such as principal component analysis (PCA) and its variations [14-16], segmented auto-encoder [17] and singular spectrum analysis (SSA) ...dimension reduction and ... See full document
14
Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... for feature extraction, such as principal component analysis (PCA) and its variations [14-16], segmented auto-encoder [17] and singular spectrum analysis (SSA) ...dimension reduction and ... See full document
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