Some new classification methods for hyperspectral remote sensing

2006 ◽  
Author(s):  
Pei-jun Du ◽  
Yun-hao Chen ◽  
Simon Jones ◽  
Jelle G. Ferwerda ◽  
Zhi-jun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Classification Methods ◽  
New Classification

scholarly journals Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”

2020 ◽  
Vol 12 (21) ◽  
pp. 3665
Author(s):  
Simone Pascucci ◽  
Stefano Pignatti ◽  
Raffaele Casa ◽  
Roshanak Darvishzadeh ◽  
Wenjiang Huang
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Hyperspectral Remote Sensing ◽  
Estimation Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Special Issue ◽  
Biophysical Parameters ◽  
Vegetation Monitoring ◽  
Practical Applications

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

scholarly journals SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

2016 ◽  
Vol III-7 ◽  
pp. 25-31
Author(s):  
Xiaobing Han ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Spectral Feature ◽  
Hyperspectral Remote Sensing ◽  
Spectral Information ◽  
Selection Strategy ◽  
Classification Methods ◽  
Spectral Classification ◽  
Remote Sensing Imagery ◽  
Central Pixel ◽  
Window Selection

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

scholarly journals SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

2016 ◽  
Vol III-7 ◽  
pp. 25-31 ◽  
Author(s):  
Xiaobing Han ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Spectral Feature ◽  
Hyperspectral Remote Sensing ◽  
Spectral Information ◽  
Selection Strategy ◽  
Classification Methods ◽  
Spectral Classification ◽  
Remote Sensing Imagery ◽  
Central Pixel ◽  
Window Selection

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology
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