An adaptive classifier for high dimensional image data anda small training sample set

Neste trabalho é testado um classificador adaptativo que visa suavizar os efeitos causados por um número insuficiente de amostras de treinamento, fato este que pode degradar severamente a acurácia dos resultados obtidos por um classificador paramétrico utilizando dados com dimensão alta. O classificador adaptativo adiciona amostras semi-rotuladas ao conjunto das amostras de treinamento com o objetivo de reduzir os efeitos causados pelo pequeno número de amostras. O efeito das amostras semi-rohlladas é controlado por meio de um peso menor do que o peso atribuído as amostras originais. Os experimentos desenvolvidos mostram que este procedimento é eficiente na redução dos efeitos do fenômeno de Hughes contribuindo para aumentar a acurácia da imagem temática produzida.In this paper, we test a self-leaming and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the accuracy of the results produced by a parametric classifier employing high dimensional image data. The adaptive classifier mitigates the small training sample size by adding semi-labeled samples to the training set. In order to control the influence of semi-labeled samples, the proposed method assigns full weight to the training samples and reduced weight to semi-labeled samples. Experiments show that this procedure is effective in mitigating the Hughes phenomenon and increasing therefore the accuracy ofthe resulting thematic ma

lnvestigation on methods for dimensionality reduction on hyperspectral image data

Neste estudo é proposta uma nova metodologia para fins de redução da dimensionalidade dos dados em imagens hiperespectrais. Nesta abordagem, parte-se da suposição de que a curva de resposta espectral em cada pixel, pode ser adequadamente descrita por um número menor de parâmetros estatísticos, em substituição aos contadores digitais (CDs) originais, para utilização em um classificador. Neste processo, a curva de resposta espectral é inicialmente particionada em um certo número de segmentos. Em uma segunda etapa, os contadores digitais (CDs) que caracterizam cada segmento são substituídos por um número menor de parâmetros estatísticos, como a média e a variância estimada a partir dos CD's em cada segmento individual. Para a segmentação da curva de resposta espectral de cada pixel, são propostos três algoritmos de fácil implementação e computacionalmente eficientes. Usando uma estratégia top-down, o comprimento de cada segmento ao longo da curva espectral pode ser ajustado seqüencialmente. Experimentos são realizados utilizando dados adquiridos pelo sensor AVIRIS. Resultados animadores são obtidos em termos de acurácia de classificação e tempo de execução, sugerindo a eficácia dos algoritmos propostos.In the present study, we propose a new simple approach to reduce the dimensionality in hyperspectral image data. The basic assumption consists in assuming that a pixel's curve of spectral response, as defined in the spectral space by the recorded digital numbers (DNs) at the available spectral bands, can be segmented and each segment can be replaced by a smaller number of statistics, e.g., the mean and the variance, describing the main characteristics of a pixel's spectral response. Results suggest that this procedure can be accomplished without signiftcant loss of information. The DNs at every spectral band can be used to estimate a few statistical parameters that will replace them in a classifier. For the pixel's spectral curve segmentation, three sub-optimal algorithms are proposed, being easy to implement and also computationally efficient. Using a top-down strategy, the length of the segments along the spectral curves can be adjusted sequentially. Experiments using a parametric classifier are performed using an AVIRIS data set. Encouraging results have been obtained in terms of classification accuracy and execution time, suggesting the effectiveness of the proposed algorithms

Árvore Binária SVM Otimizada na Classificação de Imagem Hiperespectral

In this paper we investigate an optimization procedure using the binary classifier Support Vector Machines (SVM) applied to highdimensional image data (hyperspectral image data) in a multiclass problem. In this particular case, one problem that has been investigated refers to the optimal choice for the parameters in the selected kernel function. Different approaches have been proposed in the literature for global optimization of the kernel parameters in a multiclass problem. In this study we investigate the use of a binary tree with the kernel parameters estimated at every tree node, by using the global accuracy as an optimization criterion. The proposed methodology is tested by using hyperspectral image data collected over the Indian Pine test area. The global classification accuracy yielded by the proposed methodology is compared with the results of a similar procedure implementing no optimization procedure.Pages: 2298-230

Seleção de variáveis em imagens hiperespectrais para classificadores SVM

Very recent articles report that Support Vector Machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. In this paper we investigate the performance of the SVM classifier when applied to high dimensional image data, depicting natural scenes. Since the SVM classifier deals with a pair of classes at a time, a multi-stage classifier, structured as a binary tree is proposed, comparing classification approaches based on three different feature selection methods, i.e., selection of N features at regular intervals throughout the electromagnetic spectrum, Sequential Forward Selection (SFS) and the Recursive Feature Elimination technique (RFE). The RBF kernel is used in this study. Tests are performed using AVIRIS hyperspectral image data covering a test area which includes classes spectrally very similar, separable in high-dimensional spaces only.Pages: 7729-773

Detecção de mudanças em imagens multitemporais de sensoriamento remoto empregando support vector machines

In this paper we investigate a semi-supervised approach to change detection in remote sensing multitemporal image data by applying Support Vector Machines (SVM) techniques. The proposed methodology is based on the difference between fraction images produced in two different dates. In images of natural scenes the difference in the fractions such as vegetation and bare soil occurring in two different dates tend to present a distribution symmetric about the origin. This fact can be used to model two overlapping multivariate Normal distributions: change, no-change. The Expectation-Maximization algorithm (EM) is implemented to estimate the parameters (mean vector, covariance matrix and a priori probability) associated with these two distributions. Random samples are extracted from these two distributions and used to train a SVM classifier in this semi-supervised approach. The proposed methodology is tested by using sets of multitemporal TM-Landsat multispectral image data covering the same scene in two different dates. The results are compared with the other procedures, including the available field observations.Pages: 8301-830

Uma abordagem multivariada para detecção de mudanças a partir de imagens de fração

Land cover change detection is a major goal in multitemporal remote sensing applications. It is well known that images acquired on different dates tend to be highly influenced by radiometric differences and registration problems. Using fraction images, obtained from the linear model of spectral mixing (LMSM), radiometric problems can be minimized and the interpretation of changes in land cover is facilitated because the fractions have a physical meaning. This paper explores a multivariate approach to change detection problems between a pair of fraction images that allows the definition of a probabilistic threshold for labeling pixels into class change. The algorithm creates a binary change map which can be combined with the differences in the fractions. By using a clustering algorithm it is possible creates a new map that identifies the kinds of change. The algorithm is simple and fast and does not require the choice of components since all fractions are used simultaneously. It is required that the differences of fractions are multivariate normal because the technique is based on the fact that the contours of constant density are limited by chi-square distribution, according the choice of level of significance. The results, obtained on synthetic images, indicate that the procedure is efficient.Pages: 7556-756

On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data

It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the role played by the second-order statistics in remote sensing digital image classification of natural scenes when the classes of interest are spectrally very similar and high dimensional multispectral image data is available. A number of the investigations that have been developed in this area deal with the fact that as the data dimensionality increases, so does the difficulty in obtaining a reasonably accurate estimate of the within-class covariance matrices from the number of available labeled samples, which is usually limited. Several approaches have been proposed to deal with this problem. This study aims toward a complementary goal. Assuming that reasonably accurate estimates for the withinclass covariance matrices have been obtained, we seek to better understand what kind of geometrically-oriented interpretation can be given to them as the data dimensionality increases and also to understand how this knowledge can help the design of a classifier. In order to achieve this goal, the covariance matrix is decomposed into a number of parameters that are then analyzed separately with respect to their ability to separate the classes. Methods for image classification based on these parameters are investigated. Results of tests using data provided by the sensor system AVIRIS are presented and discussed

On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data

It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the role played by the second-order statistics in remote sensing digital image classification of natural scenes when the classes of interest are spectrally very similar and high dimensional multispectral image data is available. A number of the investigations that have been developed in this area deal with the fact that as the data dimensionality increases, so does the difficulty in obtaining a reasonably accurate estimate of the within-class covariance matrices from the number of available labeled samples, which is usually limited. Several approaches have been proposed to deal with this problem. This study aims toward a complementary goal. Assuming that reasonably accurate estimates for the withinclass covariance matrices have been obtained, we seek to better understand what kind of geometrically-oriented interpretation can be given to them as the data dimensionality increases and also to understand how this knowledge can help the design of a classifier. In order to achieve this goal, the covariance matrix is decomposed into a number of parameters that are then analyzed separately with respect to their ability to separate the classes. Methods for image classification based on these parameters are investigated. Results of tests using data provided by the sensor system AVIRIS are presented and discussed

Estimação de Pesos Para Amostras Semi-Rotuladas em Classificadores Paramétricos

In this paper it is investigated a methodology aimed at the estimation of the weights to be assigned to semi-labeled samples in the training of parametric classifiers when the number of training samples (pixels) are too small for the dimensionality of the data (bands), and the estimates of mean vector and the covariance matrix are poor. The proposed methodology includes two steps, the first step is only for estimate the weight of the semi-labeled samples, and the second step is for refine the weight estimated in the previus step. Initially it is investigated a procedure to estimate weights based on the classifiers decision function, where the semi-labeled samples are weighted according to probability of belongs to determined class. Next, these estimated are further refined by means of spatial context information, by use of the probabilistic relaxation to refine the weight of these semi-labeled samples. After this refinement, the mean vector and the covariance matrix are reestimated, and the parameters are used as input data in the parametric classifier. The methodology is tested by applying it to a set of hyperspectral image data. This data are from test site which had several classes spectrally similar, and is due this characteristic of the site wich is propitious to work with hyperspectral sensors like AVIRIS.Pages: 7332-733

An adaptive image enhancement algorithm

Image enhancement is a common procedure intended to process an image so that the resulting processed image is more suitable than the original one for a given application. Spatial filtering is a well-known procedure to achieve this goal. Low-pass filtering smooths the image and is often used as a preprocessing step to image analysis, removing small details and reducing noise. This filtering process, however, blurs the image. High-pass filtering sharpens the borders between the different image regions by highlighting edges. This process also highlights noise and spurious details. This study proposes a different method that first detects borders between radiometrically distinct image regions and then applies a weighting function to smooth each region internally. The proposed algorithm was implemented on a SUN Sparcstation for testing purposes. Results of the procedure are presented