A spatial-spectral classification approach of multispectral data for ground perspective materials

DuPont, Edmond M.; Chambers, David R.; Alexander, Joseph; Alley, Kevin

doi:10.1109/icsmc.2011.6084140

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, hyperspectral data by itself may not provide comprehensive information of ground objects for certain applications. Previous research has shown that the spatial information can be utilized for a variety of image analysis tasks [1], [2]. In addition, LiDAR, an active optical remote sensing technique that measures the distance to an object by sending and receiving pulses from a laser, has been utilized to discriminate different ground classes with different elevations, and used for multiple applications, e.g., landscape level analysis of salt marsh plant habitats [3] and large area ecosystem characterization [4].…”

Section: Introductionmentioning

confidence: 99%

Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis

Zhang

Prasad

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Multi-source data, either from different sensors or disparate features extracted from the same sensor, are valuable for geospatial image analysis due to their potential for providing complementary features. In this paper, a composite-kernel-based feature extraction method is proposed for multi-source remote sensing data classification. Features from different sources are first fused via a weighted composite kernel mapping, and then projected to a lower-dimensional subspace in which kernel local Fisher discriminant analysis (KLFDA) is used to extract the most discriminative information. We hypothesize that after such a projection, multi-source data would have better class separability between classes, and an efficient linear classification modelmultinomial logistic regression (MLR) would be suitable for classification. The efficacy of the proposed method is demonstrated via experiments using two different sets of multi-source geospatial data. For feature fusion, the raw spectral data and extended multi-attribute profiles (EMAPs) derived from the hyperspectral image are used as a testbed for multi-source image analysis. The second multi-source testbed used for validation involves sensor fusion, in which the hyperspectral and light detection and ranging (LiDAR) data are utilized. Experimental results show that composite kernel local Fisher's discriminant analysis when combined with MLR based classifier (CKLFDA-MLR) is very effective at feature extraction and classification of multi-source geospatial images.

show abstract

Section: Introductionmentioning

confidence: 99%

Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis

Zhang

Prasad

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…We argue that the goal of the majority of spectral imaging analysis is material classification [3,16,17]. Indeed, once the spectral datacube is reconstructed, the spectrum at each spatial location is compared to known spectra to make a classification.…”

Section: Introductionmentioning

confidence: 99%

Experimental demonstration of an adaptive architecture for direct spectral imaging classification

et al. 2016

View full text Add to dashboard Cite

Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.

show abstract

A spatial-spectral classification approach of multispectral data for ground perspective materials

Cited by 2 publications

References 8 publications

Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis

Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis

Experimental demonstration of an adaptive architecture for direct spectral imaging classification

Contact Info

Product

Resources

About