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Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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On the generation of training samples for neural network-based mixed Pixel Classification
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XI, 2005Co-Authors: Javier Plaza, Chein-i Chang, Antonio Plaza, Rosa M. Pérez, Pablo MartínezAbstract:One of great challenges in neural network-based analysis of remotely sensed imagery is to find an adequate pool of training samples without prior knowledge for the network so that that these unsupervised training samples can describe the data. A judicious selection of training data can be tremendously difficult due to the presence of subPixel targets and mixed Pixels, particularly, when no prior knowledge is available. Surprisingly, the above issues have been largely overlooked in the past, where most of the efforts have been focused on exploring network architecture parameters such as the arrangement and number of neurons in the different layers. Very little has been done in regard to the selection of a set of good training samples for networks in mixed Pixel Classification. This paper revisits neural network-based mixed Pixel Classification from an aspect of training sample generation and further demonstrates that the selection of training samples can be more important than the choice of a specific network architecture. Since the training samples must be obtained directly from the data to be processed in an unsupervised fashion, four types of Pixels: pure Pixel, mixed Pixel, anomalous Pixel and homogeneous Pixel are used to demonstrate this concept. A pure Pixel is a Pixel whose spectral signature is completely represented by a single marterial substance as opposed to a mixed Pixel whossee spectral signature is made up of more than one material substance. A homogeneous Pixel is defined as a pixed whose spectral signature remains nearly constant subject to small variations within its surroundings. Therefore, a homogeneous Pixel can be considered as an opposite of an anomalous Pixel whose signature is spectrally distinct from the signatures of its neighboring Pixels. In this paper, various scenarios are designed for experiments to substantiate the impact of using these four types of Pixels as training samples for mixed Pixel Classification.
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Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projection
Hyperspectral Imaging, 2003Co-Authors: Chein-i ChangAbstract:The orthogonal subspace projection (OSP) for hyperspectral image Classification was first reported in (1994) and has been successfully applied to hyperspectral data exploitation since then. Its ability in subPixel detection was also demonstrated in Chapter 3. As we recall (3.9), the OSP-derived detector was given by δ OSP(r) = T P U ⊥ (r) with the scale constant κ = 1. In Chapter 6, we have seen that this scale constant κ was actually determined by the a posteriori information that was used to estimate the unknown abundance fractions. Since OSP assumed the complete knowledge of the target signature matrix M and did not estimate the abundance vector α, the scale constant κ was absent in δ OSP(r). As long as the abundance fractions detected for α provide sufficient amounts for target detection, it did not matter if a was estimated accurately. That was why OSP worked effectively for the real hyperspectral data experiments in (1994). However, this may not be true in terms of abundance estimation. So, in this chapter, the OSP in Chapter 3 is revisited for mixed Pixel Classification. It is then extended by three unconstrained least-squares subspace projection approaches, called signature subspace projection (SSP), target subspace projection (TSP) and oblique subspace projection (OBSP) where the abundance fractions of target signatures are not known a priori, but are required to be estimated from the data. The three subspace projection methods use their estimated signature abundance fractions to achieve target Classification in a mixed Pixel. As a result, they can be viewed as a posteriori OSP as opposed to the OSP in Chapter 3, which can be thought of as a priori OSP. In order to evaluate these three approaches, a least-squares estimation error is cast as a signal detection problem in the framework of the Neyman-Pearson detection theory so that the detection performance can be measured by the receiver operating characteristics (ROC) analysis.
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Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification
Hyperspectral Imaging, 2003Co-Authors: Chein-i ChangAbstract:The automatic mixed Pixel Classification (AMPC) considered in this chapter is fully computer automated and can be implemented to automatically detect and classify targets with no human intervention. Like the automatic subPixel detection discussed in Chapters 5–6 AMPC can be also categorized into unsupervised mixed Pixel Classification and anomaly Classification. The former classifies mixed Pixels in an unsupervised manner, where the required unsupervised target knowledge is the a posteriori target information generated directly from the image data as noted in Chapter 5. By contrast, the latter extends anomaly detection to anomaly Classification, in which case the detected anomalies can be classified with no need of unsupervised target knowledge. Depending upon availability of a priori target knowledge two versions of unsupervised MPC, referred to as desired target detection and Classification algorithm (DTDCA) and automatic target detection and Classification algorithm (ATDCA), are presented in this chapter. The DTDCA is applied to a situation that there is knowledge about specific targets to be classified, whereas ATCDA can be used to classify targets of interest present in an unknown image scene without a priori target knowledge. As a consequence, they result in different applications.
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Target Abundance-Constrained Mixed Pixel Classification (TACMPC)
Hyperspectral Imaging, 2003Co-Authors: Chein-i ChangAbstract:The mixed Pixel Classification (MPC), which was considered in Chapters 8 and 9 is unconstrained with no constraint imposed on the target signature abundance fractions. Consequently, the resulting abundance estimates do not necessarily reflect their true amounts of abundance. In this case, these estimates can be only used for the purpose of target detection, discrimination and Classification, but not for target quantification. In order for MPC to perform mixed Pixel quantification, we need to consider a fully constrained mixed Pixel Classification problem, which imposes two constraints on the abundance fractions of target signatures.
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Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers
Hyperspectral Imaging, 2003Co-Authors: Chein-i ChangAbstract:The target abundance-constrained mixed Pixel Classification that was considered in Chapter 10 imposed ASC and ANC on target abundance fractions. In this case, a linear mixture model is required and the target signature matrix M must be also known a priori. In this chapter, we consider the target signature-constrained mixed Pixel Classification which does not need a linear mixture model. Instead, it classifies a mixed Pixel by constraining the spectral shapes or vector directions of target signatures rather than target abundance fractions. Such concept was explored in Chapter 4 and referred to as linearly constrained minimum variance (LCMV) approach. It was used to design target signature-constrained subPixel detectors, CEM and TCIMF. A subPixel detector can detect targets, but does not necessarily imply that it can also classify the targets it detected. It may occur that a detector can detect all targets of interest but cannot discriminate one from another. In this case, the detection rate can be as high as 100%, but the Classification rate could as low as 0%. In order for LCMV-based detectors to also achieve target Classification, they must be implemented one target at a time so that the detected targets can be classified in a separate image. This chapter extends LCMV-based detectors to LCMV classifiers. It develops an approach that expands the capability of the LCMV-based detectors in such a fashion that it can simultaneously detect and classify multiple targets in a single image where different colors are used to highlight distinct types of detected targets. In particular, such color assignment approach also allows us to extend a CEM-based detector in Chapter 4 to a CEM-based classifier. Despite that an LCMV classifier requires the prior knowledge of desired targets, it can take advantage of the unsupervised algorithms presented in Chapter 5 to generate the necessary target knowledge and make its Classification unsupervised.
John C. Kieffer - One of the best experts on this subject based on the ideXlab platform.
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Embedded image compression based on wavelet Pixel Classification and sorting
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2004Co-Authors: Kewu Peng, John C. KiefferAbstract:The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "Pixel Classification," the relationship between wavelet Pixels in significance coding. Similarly, the ordering is limited to "Pixel sorting," the coding order of wavelet Pixels. We use Pixel Classification and sorting to provide a better understanding of previous works. The image Pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel Pixel Classification and sorting (PCAS) scheme in wavelet domain. In PCAS, Pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of Pixel Classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed Pixel Classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.
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embedded image compression based on wavelet Pixel Classification and sorting
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Kewu Peng, John C. KiefferAbstract:A new embedded image compression algorithm is proposed, based on progressive Pixel Classification And Sorting (PCAS) in wavelet domain. To exploit the intraband and interband correlation in wavelet domain, EZW [1] and SPIHT [2] implicitly classify wavelet Pixels as zerotree Pixels or not, while MRWD[3], SLCCA[4], and EBCOT[5] implicitly classify wavelet Pixels as neighbors of significant Pixels or not. In this paper, the wavelet Pixels to be encoded are explicitly and finely classified based on their predicted probabilities, which is more sophisticated and effective. Furthermore, wavelet Pixel sorting is introduced to help improve rate-distortion performance within each bit-plane coding. The technique of Pixel Classification and sorting is simple, yet effective to produce the image code with excellent compression performance. In addition, our algorithm provides both SNR and resolution scalability.
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ICASSP - Embedded image compression based on wavelet Pixel Classification and sorting
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Kewu Peng, John C. KiefferAbstract:A new embedded image compression algorithm is proposed, based on progressive Pixel Classification And Sorting (PCAS) in wavelet domain. To exploit the intraband and interband correlation in wavelet domain, EZW [1] and SPIHT [2] implicitly classify wavelet Pixels as zerotree Pixels or not, while MRWD[3], SLCCA[4], and EBCOT[5] implicitly classify wavelet Pixels as neighbors of significant Pixels or not. In this paper, the wavelet Pixels to be encoded are explicitly and finely classified based on their predicted probabilities, which is more sophisticated and effective. Furthermore, wavelet Pixel sorting is introduced to help improve rate-distortion performance within each bit-plane coding. The technique of Pixel Classification and sorting is simple, yet effective to produce the image code with excellent compression performance. In addition, our algorithm provides both SNR and resolution scalability.
Jean Cousty - One of the best experts on this subject based on the ideXlab platform.
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A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification
2011Co-Authors: Kleber Souza, Silvio Guimarães, Zenilton Patrocino Jr., Arnaldo De Albuquerque Araujo, Jean CoustyAbstract:In this paper, we propose a new approach for color image simplification in order to improve flame Pixel Classification. The fire detection performance depends critically on the performance of the flame Pixel classifier. Color image simplification is the process of simplifying an image in order to decrease the number of colors while preserving, as much as possible, shapes. In this work, a hierarchical clustering method in a given color space is used to map the original colors into a smaller set of representative ones, allowing the use of a simple heuristic rule for classifying the clusters related to candidate flame colors. Using reverse mapping, we identify possible flame colors in the image. Main contributions of our work are the application of a simple hierarchical clustering method to color simplification, that decreases the number of possible flame colors, and a filtering methodology to reduce the influence of outliers. Several color spaces and distance measures were used to evaluate the proposed method. Experimental results demonstrate that color simplification is essential to successfully employ heuristic Classification of flame colors.
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ICTAI - A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification
2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011Co-Authors: Kleber J. F. Souza, Arnaldo De Albuquerque Araujo, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves Do Patrocínio, Jean CoustyAbstract:In this paper, we propose a new approach for color image simplification in order to improve flame Pixel Classification. The fire detection performance depends critically on the performance of the flame Pixel classifier. Color image simplification is the process of simplifying an image in order to decrease the number of colors while preserving, as much as possible, shapes. In this work, a hierarchical clustering method in a given color space is used to map the original colors into a smaller set of representative ones, allowing the use of a simple heuristic rule for classifying the clusters related to candidate flame colors. Using reverse mapping, we identify possible flame colors in the image. Main contributions of our work are the application of a simple hierarchical clustering method to color simplification, that decreases the number of possible flame colors, and a filtering methodology to reduce the influence of outliers. Several color spaces and distance measures were used to evaluate the proposed method. Experimental results demonstrate that color simplification is essential to successfully employ heuristic Classification of flame colors.
Kewu Peng - One of the best experts on this subject based on the ideXlab platform.
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Embedded image compression based on wavelet Pixel Classification and sorting
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2004Co-Authors: Kewu Peng, John C. KiefferAbstract:The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "Pixel Classification," the relationship between wavelet Pixels in significance coding. Similarly, the ordering is limited to "Pixel sorting," the coding order of wavelet Pixels. We use Pixel Classification and sorting to provide a better understanding of previous works. The image Pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel Pixel Classification and sorting (PCAS) scheme in wavelet domain. In PCAS, Pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of Pixel Classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed Pixel Classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.
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embedded image compression based on wavelet Pixel Classification and sorting
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Kewu Peng, John C. KiefferAbstract:A new embedded image compression algorithm is proposed, based on progressive Pixel Classification And Sorting (PCAS) in wavelet domain. To exploit the intraband and interband correlation in wavelet domain, EZW [1] and SPIHT [2] implicitly classify wavelet Pixels as zerotree Pixels or not, while MRWD[3], SLCCA[4], and EBCOT[5] implicitly classify wavelet Pixels as neighbors of significant Pixels or not. In this paper, the wavelet Pixels to be encoded are explicitly and finely classified based on their predicted probabilities, which is more sophisticated and effective. Furthermore, wavelet Pixel sorting is introduced to help improve rate-distortion performance within each bit-plane coding. The technique of Pixel Classification and sorting is simple, yet effective to produce the image code with excellent compression performance. In addition, our algorithm provides both SNR and resolution scalability.
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ICASSP - Embedded image compression based on wavelet Pixel Classification and sorting
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Kewu Peng, John C. KiefferAbstract:A new embedded image compression algorithm is proposed, based on progressive Pixel Classification And Sorting (PCAS) in wavelet domain. To exploit the intraband and interband correlation in wavelet domain, EZW [1] and SPIHT [2] implicitly classify wavelet Pixels as zerotree Pixels or not, while MRWD[3], SLCCA[4], and EBCOT[5] implicitly classify wavelet Pixels as neighbors of significant Pixels or not. In this paper, the wavelet Pixels to be encoded are explicitly and finely classified based on their predicted probabilities, which is more sophisticated and effective. Furthermore, wavelet Pixel sorting is introduced to help improve rate-distortion performance within each bit-plane coding. The technique of Pixel Classification and sorting is simple, yet effective to produce the image code with excellent compression performance. In addition, our algorithm provides both SNR and resolution scalability.
Kleber J. F. Souza - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification
2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011Co-Authors: Kleber J. F. Souza, Arnaldo De Albuquerque Araujo, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves Do Patrocínio, Jean CoustyAbstract:In this paper, we propose a new approach for color image simplification in order to improve flame Pixel Classification. The fire detection performance depends critically on the performance of the flame Pixel classifier. Color image simplification is the process of simplifying an image in order to decrease the number of colors while preserving, as much as possible, shapes. In this work, a hierarchical clustering method in a given color space is used to map the original colors into a smaller set of representative ones, allowing the use of a simple heuristic rule for classifying the clusters related to candidate flame colors. Using reverse mapping, we identify possible flame colors in the image. Main contributions of our work are the application of a simple hierarchical clustering method to color simplification, that decreases the number of possible flame colors, and a filtering methodology to reduce the influence of outliers. Several color spaces and distance measures were used to evaluate the proposed method. Experimental results demonstrate that color simplification is essential to successfully employ heuristic Classification of flame colors.