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A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data
A comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information indicates that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied. Expand
Spatial/spectral endmember extraction by multidimensional morphological operations
A new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. Expand
Advanced Spectral Classifiers for Hyperspectral Images: A review
The classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data. Expand
On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms
A comparative analysis of endmember extraction algorithms without the pure pixel assumption is provided, which uses synthetic hyperspectral data sets and real hyperspectrals collected by NASA’s Jet Propulsion Laboratory. Expand
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations
Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately. Expand
Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling
The experimental results demonstrate that the proposed multilayer stacked covariance pooling method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods. Expand
A new deep convolutional neural network for fast hyperspectral image classification
A new CNN architecture for the classification of hyperspectral images is presented, a 3-D network that uses both spectral and spatial information and implements a border mirroring strategy to effectively process border areas in the image. Expand
Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification
A new deep CNN architecture specially designed for the HSI data is presented to improve the spectral–spatial features uncovered by the convolutional filters of the network and is able to provide competitive advantages over the state-of-the-art HSI classification methods. Expand
A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation
In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information inExpand
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
Rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide. Expand