Advertisement

matlab_hyperspectral_toolbox_0.04版本

  • 5星
  •     浏览量: 0
  •     大小:None
  •      文件类型:None


简介:
Matlab_HyperSpectral_Toolbox是一款专为高光谱数据分析设计的工具箱,其0.04版更新了算法优化和多项新功能。 % Matlab Hyperspectral Toolbox % Copyright 2008-2009 Isaac Gerg %% ------------------------------------------------------------------------ % A Note on Notation Hyperspectral data is often expressed in various ways to better describe the mathematical handling of the data, mainly as a vector of pixels when referring to the data in space or a matrix of pixels when referring to it as an image. For consistency, common notation is defined to differentiate these concepts clearly. Hyperspectral data examined like an image will be defined as a matrix Mm譶譸 with dimensions m �n �p where m represents the number of rows in the image, n represents the number of columns in the image, and p represents the number of bands in the image. Therefore, a single element of such an image is accessed using Mi,j,k and a single pixel is accessed using Mi,j,: Hyperspectral data formed as a vector of vectors (i.e., 2D matrix) will be defined as M(m穘)譸 with dimensions (m�n)譸. A single element can be accessed using Mi,j, while a single pixel can be accessed using M:,j. Notice the multi-element notation is consistent with MatlabTM; this consistency was intentional. The list below provides a summary of the notation convention used throughout this code. M: Data matrix. Defined as an image of spectral signatures or vectors (either Mm譶譸 or M(m穘)譸). N: The total number of pixels, for example N = m �n. m: Number of rows in the image. n: Number of columns in the image. p: Number of bands. q: Number of classes / endmembers. U: Matrix of endmembers; each column represents an endmember vector. b: Observation vector, a single pixel. x: Weight vector; a matrix of weight vectors forms an abundance map. %% ------------------------------------------------------------------------ % Dependencies FastICA %% ------------------------------------------------------------------------ % Functions Reading/Writing Data Files: - hyperReadAvirisRfl - Reads AVIRIS .rfl files. - hyperReadAvirisSpc - Read AVIRIS .spc files. - hyperReadAsd - Reads ASD Fieldspec files (.asd, .000, etc). Data Formatting: - hyperConvert2D - Converts data from a 3D HSI data cube to a 2D matrix. - hyperConvert3D - Converts data from a 2D matrix to a 3D HSI data cube. - hyperNormalize - Normalizes data to be in range of [0,1]. - hyperConvert2Jet - Converts a 2D matrix to jet colormap values. - hyperResample - Resamples hyperspectral data to new wavelength set. Unmixing: - hyperAtgp - ATGP algorithm. - hyperIcaEea - ICA-Endmember Extraction Algorithm. - hyperIcaComponentScores - Computes ICA component scores for relevance. - hyperVca - Vertex Component Analysis. - hyperPPI - Pixel Purity Index. Target Detection: - hyperACE - Adaptive cosine/coherent estimator. - hyperGLRT - Generalized likelihood ratio test. - hyperHUD - Hybrid instructured detector. - hyperAMSD - Adaptive matched subspace detector. - hyperMatchedFilter - Matched filter. - hyperOsp - Orthogonal subspace projection. - hyperCem - Constrained energy minimization. Material Count Estimation: - hyperHfcVd: Computes virtual dimensionality (VD) using HFC method. Data Conditioning: - hyperPct: Principal component transform. - hyperMnf: Minimum noise fraction. - hyperDestreak - Destreaking algorithm. Abundance Map Generation: - hyperUcls - Unconstrained least squares. - hyperNnls - Non-negative least squares. - hyperFcls - Fully constrained least squares. Spectral Measuring: - hyperSam: Spectral Angle Mapper. - hyperSid: Spectral Information Divergence. - hyperNormXCorr: Normalized Cross Correlation. Miscellaneous Functions: hyperMax2d - Finds the max value and corresponding position in a matrix. Sensor Specific: hyperGetHymapWavelengthsNm - Returns list of Hymap wavelengths. Statistics: - hyperCov: Sample covariance matrix estimator. - hyperCorr: Sample autocorrelation matrix estimator. Demos: - hyperDemo - General toolbox usage. - hyperDemo_detectors - Target detection algorithms. - hyperDemo_RIT_data - RIT target detection blind test. - hyperDemo_ASD_reader - Reads ASD Fieldspec files.

全部评论 (0)

还没有任何评论哟~
客服
客服
  • matlab_hyperspectral_toolbox_0.04
    优质
    Matlab_HyperSpectral_Toolbox是一款专为高光谱数据分析设计的工具箱,其0.04版更新了算法优化和多项新功能。 % Matlab Hyperspectral Toolbox % Copyright 2008-2009 Isaac Gerg %% ------------------------------------------------------------------------ % A Note on Notation Hyperspectral data is often expressed in various ways to better describe the mathematical handling of the data, mainly as a vector of pixels when referring to the data in space or a matrix of pixels when referring to it as an image. For consistency, common notation is defined to differentiate these concepts clearly. Hyperspectral data examined like an image will be defined as a matrix Mm譶譸 with dimensions m �n �p where m represents the number of rows in the image, n represents the number of columns in the image, and p represents the number of bands in the image. Therefore, a single element of such an image is accessed using Mi,j,k and a single pixel is accessed using Mi,j,: Hyperspectral data formed as a vector of vectors (i.e., 2D matrix) will be defined as M(m穘)譸 with dimensions (m�n)譸. A single element can be accessed using Mi,j, while a single pixel can be accessed using M:,j. Notice the multi-element notation is consistent with MatlabTM; this consistency was intentional. The list below provides a summary of the notation convention used throughout this code. M: Data matrix. Defined as an image of spectral signatures or vectors (either Mm譶譸 or M(m穘)譸). N: The total number of pixels, for example N = m �n. m: Number of rows in the image. n: Number of columns in the image. p: Number of bands. q: Number of classes / endmembers. U: Matrix of endmembers; each column represents an endmember vector. b: Observation vector, a single pixel. x: Weight vector; a matrix of weight vectors forms an abundance map. %% ------------------------------------------------------------------------ % Dependencies FastICA %% ------------------------------------------------------------------------ % Functions Reading/Writing Data Files: - hyperReadAvirisRfl - Reads AVIRIS .rfl files. - hyperReadAvirisSpc - Read AVIRIS .spc files. - hyperReadAsd - Reads ASD Fieldspec files (.asd, .000, etc). Data Formatting: - hyperConvert2D - Converts data from a 3D HSI data cube to a 2D matrix. - hyperConvert3D - Converts data from a 2D matrix to a 3D HSI data cube. - hyperNormalize - Normalizes data to be in range of [0,1]. - hyperConvert2Jet - Converts a 2D matrix to jet colormap values. - hyperResample - Resamples hyperspectral data to new wavelength set. Unmixing: - hyperAtgp - ATGP algorithm. - hyperIcaEea - ICA-Endmember Extraction Algorithm. - hyperIcaComponentScores - Computes ICA component scores for relevance. - hyperVca - Vertex Component Analysis. - hyperPPI - Pixel Purity Index. Target Detection: - hyperACE - Adaptive cosine/coherent estimator. - hyperGLRT - Generalized likelihood ratio test. - hyperHUD - Hybrid instructured detector. - hyperAMSD - Adaptive matched subspace detector. - hyperMatchedFilter - Matched filter. - hyperOsp - Orthogonal subspace projection. - hyperCem - Constrained energy minimization. Material Count Estimation: - hyperHfcVd: Computes virtual dimensionality (VD) using HFC method. Data Conditioning: - hyperPct: Principal component transform. - hyperMnf: Minimum noise fraction. - hyperDestreak - Destreaking algorithm. Abundance Map Generation: - hyperUcls - Unconstrained least squares. - hyperNnls - Non-negative least squares. - hyperFcls - Fully constrained least squares. Spectral Measuring: - hyperSam: Spectral Angle Mapper. - hyperSid: Spectral Information Divergence. - hyperNormXCorr: Normalized Cross Correlation. Miscellaneous Functions: hyperMax2d - Finds the max value and corresponding position in a matrix. Sensor Specific: hyperGetHymapWavelengthsNm - Returns list of Hymap wavelengths. Statistics: - hyperCov: Sample covariance matrix estimator. - hyperCorr: Sample autocorrelation matrix estimator. Demos: - hyperDemo - General toolbox usage. - hyperDemo_detectors - Target detection algorithms. - hyperDemo_RIT_data - RIT target detection blind test. - hyperDemo_ASD_reader - Reads ASD Fieldspec files.
  • ADB1.0.40(40)
    优质
    ADB版本1.0.40是一款针对开发者和高级用户设计的Android调试桥工具,提供了一系列增强的功能和稳定性改进。该版本优化了设备连接性能,并修复了多个已知问题,为用户提供更流畅、可靠的开发体验。 ADB(Android Debug Bridge)是连接Android手机与PC端的桥梁,在安卓平台上用于调试目的。通过adb可以管理、操作模拟器和设备,例如安装软件、查看设备软硬件参数、系统升级以及运行shell命令等。当前版本为1.0.40,适用于Windows环境。
  • 3D Max转换,模型互导,兼容2009,高转低
    优质
    本工具支持将3D Max不同版本间的模型进行相互转换,特别兼容从高版本到2009版的降级转换,确保文件在旧版本软件中的顺利打开与编辑。 可以跨版本复制拷贝3dMax模型,并支持不同版本之间的互导操作。理论上各个版本都适用,包括2009版的max文件也应能兼容。在进行3D Max版本转换时,请确保VR材质能够正常导入和导出。
  • Python 3.7 免安装
    优质
    Python 3.7 版本免安装版本 提供了一个便捷的方式让开发者无需繁琐的安装步骤即可快速开始使用 Python 3.7 的强大功能,特别适合初学者和临时项目需求。 不需要安装任何软件包,包括Python的原始安装包。希望与大家多多交流分享。
  • .apk和新.apk
    优质
    《旧版本.apk与新版本.apk》:探讨应用软件从旧版升级到新版的变化,包括功能改进、性能优化及新增特性等。帮助用户了解更新内容,决定是否进行升级。 用于增量更新的准备工作涉及new.apk与old.apk。
  • ARCM2引擎,XE10.3 BLUE
    优质
    这款软件基于ARCM2引擎和XE10.3 BLUE版本开发,提供了强大的功能和优化体验,适用于各种复杂项目需求。 这只是一个版本,程序源码在另一个我上传的资源里,有兴趣的话可以去下载。程序由MYLOVE更新,最新的XE10.3BLUE已经发布,全民MYSQL BLUE开启,一起进入XE时代。
  • Python 3.7的64位
    优质
    Python 3.7的64位版本提供了增强的数据处理能力和更大的内存空间支持,适用于需要高性能计算和大型数据集处理的应用场景。 Python 3.7版本适用于64位计算机,并且我已经成功安装并可以使用。使用说明可以在网上自行查找,与之前的版本安装方法相同。
  • 最新的JRebel破解:7.1.2
    优质
    请注意,使用或分发如JRebel等软件的破解版是违反版权法和许可协议的行为。本段并非推荐此类行为,而是针对特定请求提供信息。JRebel 7.1.2破解版声称可以为开发人员在Java应用开发中提供即时代码变更重载功能,避免繁琐的应用重启流程,提高工作效率。然而,请开发者们注意选择合法的软件获取途径,支持正版以维护良好的软件生态。 最新的JRebel 7.1.2 破解版 JRebel破解版 最新的JRebel 7.1.2 破解版 JRebel破解版 最新的JRebel 7.1.2 破解版