Fusion of airborne polarimetric and interferometric SAR for classification of coastal environments

Crawford, Melba M.; Kumar, Shailesh; Ricard, M.; Gibeaut, James C.; Neuenschwander, A. L.

doi:10.1109/36.763293

Cited by 42 publications

(21 citation statements)

References 25 publications

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“…The Selective Principal Component-Synthetic Aperature Radar (SPC-SAR) product was used to fuse Landsat TM bands with fine mode radar data, represented through the Intensity-Hue-Saturation color transform. Other studies have utilized data fusion to examine landcover and topographic features (e.g., Crawford et al, 1999), and this approach has great potential, as information from existing and new sensors and GIS databases can be combined to facilitate DGM.…”

Section: Data Fusionmentioning

confidence: 99%

Geospatial technologies and digital geomorphological mapping: Concepts, issues and research

et al. 2012

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Section: Data Fusionmentioning

confidence: 99%

Geospatial technologies and digital geomorphological mapping: Concepts, issues and research

et al. 2012

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“…Both these algorithms were used in a pair wise classifier framework where the original C class problem was divided into a 2 class problem. Bayesian pair wise classifier framework [18], [19] as shown in Fig. 3 was used to decompose a C class problem into a set of 2 class problem for all pairs (wi,wj), 1<i<j<C .…”

Section: Classification Techniques Used In Hyperspectral Images 21bamentioning

confidence: 99%

A Survey on Trends and Techniques used in Hyperspectral Image Processing

Sarmah¹,

Kalita²

2015

IJCA

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Recent advancement in remote sensing facilitates collection of hyperspectral images (HSIs) in hundreds of bands which provides a potential platform to detect and identify the unique trends in land and atmospheric datasets with high accuracy. But along with the detailed information, HSIs also pose several processing problems such as-1) increase in computational complexity due to high dimensionality. So dimension reduction without losing information is one of the major concerns in this area and 2) limited availability of labeled training sets causes the ill posed problem which is needed to be addressed by the classification algorithms.Initially classification techniques of HSIs were based on spectral information only. Gradually researchers started utilizing both spectral and spatial information to increase classification accuracy. Also the classification algorithms have evolved from supervised to semi supervised mode. This paper presents a survey about the techniques available in the field of HSI processing to provide a seminal view of how the field of HSI analysis has evolved over the last few decades and also provides a snapshot of the state of the art techniques used in this area.

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“…However, the high dimensionality of the data is problematic for supervised statistical classification techniques that utilize the estimated covariance matrix since the number of known samples is typically small relative to the dimension of the data [1]. Previous research has dealt with this problem using a) regularization methods to stabilize the estimated covariance matrix directly or by using the pseudo-inverse [2,3], b) transformation of the input space via reduction in the dimension of the feature space via feature extraction or selection [4,5] or addition of artificially labeled data [6,7], and c) utilization of ensembles of classifiers (e.g. bagging, simple random sub-sampling, arcing) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Best bases Bayesian hierarchical classifier for hyperspectral data analysis

Morgan¹,

Henneguelle

Crawford³

et al.

IEEE International Geoscience and Remote Sensing Symposium

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Abstract. Classification of hyperspectral data is challenging because of high dimensionality inputs coupled with possible high dimensional outputs and scarcity of labeled information. Previously, a multiclassifier system was formulated in a binary hierarchical framework to group classes for accurate, rapid discrimination. In order to improve performance for small sample sizes, a new approach was developed that utilizes a feature reduction scheme which adaptively adjusts to the amount of labeled data available, while exploiting the fact that certain adjacent hyperspectral bands are highly correlated. The resulting best-basis binary hierarchical classifier (BB-BHC) family is thus able to address the "small sample size" problem, as evidenced by experimental results obtained from analysis of AVIRIS and Hyperion data acquired over Kennedy Space Center.

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Fusion of airborne polarimetric and interferometric SAR for classification of coastal environments

Cited by 42 publications

References 25 publications

Geospatial technologies and digital geomorphological mapping: Concepts, issues and research

Geospatial technologies and digital geomorphological mapping: Concepts, issues and research

A Survey on Trends and Techniques used in Hyperspectral Image Processing

Best bases Bayesian hierarchical classifier for hyperspectral data analysis

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