Dora Blanco Heras

Spectral–Spatial Classification of Hyperspectral Images Using Wavelets and Extended Morphological Profiles

This paper deals with hyperspectral image classification in remote sensing. The proposed scheme is a spectral-spatial technique based on wavelet transforms and mathematical morphology. The original contribution of this paper is that the extended morphological profile (EMP) is created from the features extracted by wavelets, which has proven to be better or comparable to other techniques for dimensionality reduction of hyperspectral data. In addition, the hyperspectral image is denoised, also using wavelets, with the objective of removing undesirable artifacts introduced in the acquisition of the data. The classification is carried out by a support vector machine (SVM) classifier. The accuracy is improved when comparing with previously developed spectral-spatial SVM-based schemes.

Improving the locality of the sparse matrix-vector product on shared memory multiprocessors

We extend a model of locality and the subsequent process of locality improvement previously developed for the case of sparse algebra codes in monoprocessors to the case of NUMA shared memory multiprocessors (SMPs). In particular the product of a sparse matrix by a dense vector (SpM/spl times/V) is studied. In the model, locality is established at run-time considering parameters that describe the structure of the sparse matrix involved in the computations. The problem of increasing the locality is formulated as a graph problem, whose solution indicates some appropriate reordering of rows and columns of the sparse matrix. The reordering algorithms were tested for a broad set of matrices. We have also performed a comparison with other reordering algorithms. The results lead to general conclusions about improving SMP performance for other sparse algebra codes.

Exploring ELM-based spatial–spectral classification of hyperspectral images

Among the different computational intelligence techniques avalaible for hyperspectral data classification, support vector machines (SVMs) have played a dominant role. Recently, a new learning algorithm for single-layer feedforward neural networks called the extreme learning machine (ELM) was proposed. This technique is competitive with SVMs in terms of accuracy, learning speed, and computational scalability. In this article, we propose and evaluate the use of ELM for land-cover classification from hyperspectral images. In addition, we consider two ELM-based techniques integrating spectral and spatial information of the image. The first is a scheme that uses a majority vote approach in order to combine the results of a pixel-wise spectral classification by ELM and a segmentation map obtained by a watershed algorithm. The second introduces spatial information from a small spatial neighbourhood after the classification by ELM. We show the usefulness of spatial–spectral ELM-based classification techniques in hyperspectral imaging. The results are compared to those obtained by similar SVM-based techniques and show improved classification results and much lower execution time. These simple and computationally cheap methods can be combined with others traditionally applied to hyperspectral images.

Efficient ELM-Based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs

Extreme learning machine (ELM) is an efficient learning algorithm that has been recently applied to hyperspectral image classification. In this paper, the first implementation of the ELM algorithm fully developed for graphical processing unit (GPU) is presented. ELM can be expressed in terms of matrix operations so as to take advantage of the single instruction multiple data (SIMD) computing paradigm of the GPU architecture. Additionally, several techniques like the use of ensembles, a spatial regularization algorithm, and a spectral-spatial classification scheme are applied and projected to GPU in order to improve the accuracy results of the ELM classifier. In the last case, the spatial processing is based on the segmentation of the hyperspectral image through a watershed transform. The experiments are performed on remote sensing data for land cover applications achieving competitive accuracy results compared to analogous support vector machine (SVM) strategies with significantly lower execution times. The best accuracy results are obtained with the spectral-spatial scheme based on applying watershed and a spatially regularized ELM.

Performance optimization of irregular codes based on the combination of reordering and blocking techniques

The combination of techniques based on reordering data with classic code restructuring techniques for increasing the locality in the execution of sparse algebra codes is studied in this paper. The reordering techniques are based on, first modeling the locality in run-time, and then applying a heuristic for increasing it. After this, a code restructuring technique specially tuned for sparse algebra codes called register blocking is applied. The product of a sparse matrix by a dense vector (SpMxV) is the code studied on different monoprocessors and distributed memory multiprocessors. The combination of both techniques was tested for a broad set of matrices from real problems and known repositories. The results expressed in terms of execution time show that an adequate reordering of the data improves the efficiency of applying register blocking, therefore, reducing the execution time for the sparse algebra code considered.

Towards real-time hyperspectral image processing, a GP-GPU implementation of target identification

In the quest for real time processing of hyperspectral images, this paper presents two artificial intelligence algorithms for target detection specially developed for their implementation over GPU and applied to a search-and-rescue scenario. Both algorithms are based on the application of artificial neural networks to the hyperspectral data. In the first algorithm the neural networks are applied at the level of individual pixels of the image. The second algorithm is a multiresolution based approach to scale invariant target identification using a hierarchical artificial neural network architecture. We have studied the main issues for the efficient implementation of the algorithms in GPU: the exploitation of thousands of threads that are available in this architecture and the adequate use of bandwidth of the device. The tests we have performed show both the effectiveness of detection of the algorithms and the efficiency of the GPU implementation in terms of execution times.

Modeling data locality for the sparse matrix-vector product using distance measures

Abstract In this work, we model the data locality in the execution of codes with irregular accesses. We focus on the product of a sparse matrix by a dense vector (SpM×V). In the model, locality is established taking into account pairs of rows or columns of sparse matrices. In order to evaluate this locality three functions are introduced based on two parameters: number of entry matches and number of block matches. The model is generalized considering windows of locality (groups of consecutive rows/columns of the matrix). We show results for a broad set of matrices measuring the goodness of our predictions of locality.

Modeling and improving locality for the sparse-matrix-vector product on cache memories

Abstract A model for representing and improving the locality exhibited by the execution of sparse irregular problems is developed in this work. We focus on the product of a sparse matrix by a dense vector (SpM×V). We consider the cache memory as a representative level of the memory hierarchy. Locality is evaluated through four functions based on two parameters called entry matches and line matches. In order to increase the locality, two algorithms are applied: one based on the construction of minimum spanning trees and the other on the nearest-neighbor heuristic. These techniques were tested and compared with some standard ordering algorithms.

Modeling and Improving Locality for Irregular Problems: Sparse Matrix-Vector Product on Cache Memories as a Cache Study

In this paper we introduce a model for representing and improving the locality of sparse matrices for irregular problems. We focus our attention on the behavior of iterative methods for the solution of sparse linear systems with irregular patterns. In particular the product of a sparse matrix by a dense vector (SpM×V) is closely examined, as this is one of the basic kernels in such codes. As a representative level of the memory hierarchy, we consider the cache memory. In our model, locality is measured taking into account pairs of rows or columns of sparse matrices. In order to evaluate this locality four functions based on two parameters called entry matches and cache line matches are introduced. Using an analogy of this problem to the Traveling Salesman Problem we have applied two algorithms in order to solve it; one based on the construction of minimum spanning trees and the other on the nearest-neighbor heuristic. These techniques were tested over a set of sparse matrices. The results were assesed through the measurement of cache misse on a standard cache memory.

ELM-based spectral–spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings

Extreme Learning Machine (ELM) is a supervised learning technique for a class of feedforward neural networks with random weights that has recently been used with success for the classification of hyperspectral images. In this work, we show that the morphological techniques can be integrated in this kind of classifiers using several composite feature mappings which are proposed for ELM. In particular, we present a spectral–spatial ELM-based classifier for hyperspectral remote-sensing images that integrates the information provided by extended morphological profiles. The proposed spectral–spatial classifier allows different weights for both spatial and spectral features, outperforming other ELM-based classifiers in terms of accuracy for land-cover applications. The accuracy classification results are also better than those obtained by equivalent spectral–spatial Support-Vector-Machine-based classifiers.