초분광 영상 처리

초분광 데이터 가져오기 내보내기, 처리 및 시각화

Image Processing Toolbox™ Hyperspectral Imaging Library는 초분광 영상 처리 및 시각화를 위한 MATLAB^® 함수와 툴을 제공합니다.

이 라이브러리의 함수를 사용하면 초분광 영상 센서를 사용하여 다양한 파일 형식으로 캡처된 초분광 데이터를 읽고, 쓰고, 처리할 수 있습니다. 라이브러리는 NITF(National Imagery Transmission Format ), ENVI(Environment for Visualizing Images), TIFF(Tagged Image File Format) 및 MTL(Metadata Text Extension) 파일 형식을 지원합니다.

이 라이브러리는 엔드멤버 추출, 구성 비율 맵(abundance map) 추정, 복사 보정 및 대기 보정, 차원 축소, 대역 선택, 스펙트럼 매칭 및 이상 감지를 위한 알고리즘을 제공합니다.

초분광 뷰어 앱을 사용하여 초분광 데이터를 읽어 들이고, 개별 대역 영상과 그 히스토그램을 시각화하고, 초분광 데이터 큐브의 픽셀 또는 영역에 대해 스펙트럼 플롯을 만들고, 초분광 영상의 색상 또는 가색상(false color) 표현을 생성하고, 메타데이터를 표시할 수 있습니다.

초분광 영상 분석을 수행하려면 애드온 탐색기에서 Image Processing Toolbox Hyperspectral Imaging Library를 다운로드하십시오. 애드온 다운로드에 대한 자세한 내용은 애드온을 받고 관리하기를 참조하십시오.

앱

초분광 뷰어

초분광 데이터 시각화

함수

모두 확장

탐색, 분석, 시각화

읽기와 쓰기

`hypercube`	Read hyperspectral data
`enviwrite`	Write hyperspectral data to ENVI file format
`enviinfo`	Read metadata from ENVI header file

대역 선택 및 대역 제거

`selectBands`	Select most informative bands
`removeBands`	Remove spectral bands from data cube

ROI 선택

`assignData`	Assign new data to hyperspectral data cube
`cropData`	Crop regions-of-interest

색 변환

colorize Estimate color image of hyperspectral data

필터링 및 향상

`denoiseNGMeet`	Denoise hyperspectral images using non-local meets global approach
`sharpencnmf`	Sharpen hyperspectral data using coupled nonnegative matrix factorization (CNMF) method

데이터 보정

복사 보정

`dn2radiance`	Convert digital number to radiance
`dn2reflectance`	Convert digital number to reflectance
`radiance2Reflectance`	Convert radiance to reflectance

대기 보정

`correctOOB`	Correct out-of-band effect using sensor spectral response
`empiricalLine`	Empirical line calibration of hyperspectral data
`fastInScene`	Perform fast in-scene atmospheric correction
`flatField`	Apply flat field correction to hyperspectral data cube
`iarr`	Apply internal average relative reflectance (IARR) correction to hyperspectral data cube
`logResiduals`	Apply log residual correction to hyperspectral data cube
`rrs`	Compute remote sensing reflectance
`subtractDarkPixel`	Subtract dark pixel value from hyperspectral data cube
`sharc`	Perform atmospheric correction using satellite hypercube atmospheric rapid correction (SHARC)

스펙트럼 보정

`smileMetric`	Compute spectral smile metrics of hyperspectral data
`reduceSmile`	Reduce spectral smile effect in hyperspectral data cube

차원 축소

`hyperpca`	Principal component analysis of hyperspectral data
`hypermnf`	Maximum noise fraction transform of hyperspectral data
`inverseProjection`	Reconstruct data cube from principal component bands

분광 순수화

`ppi`	Extract endmember signatures using pixel purity index
`fippi`	Extract endmember signatures using fast iterative pixel purity index
`nfindr`	Extract endmember signatures using N-FINDR
`countEndmembersHFC`	Find number of endmembers
`estimateAbundanceLS`	Estimate abundance maps

스펙트럼 매칭 및 목표 검출

`readEcostressSig`	Read data from ECOSTRESS spectral library
`sam`	Measure spectral similarity using spectral angle mapper
`sid`	Measure spectral similarity using spectral information divergence
`jmsam`	Measure spectral similarity using Jeffries Matusita-Spectral Angle Mapper method
`sidsam`	Measure spectral similarity using spectral information divergence-spectral angle mapper hybrid method
`ns3`	Measure normalized spectral similarity score
`spectralMatch`	Identify unknown regions or materials using spectral library
`spectralIndices`	Compute hyperspectral indices
`ndvi`	Normalized difference vegetation index
`anomalyRX`	Detect anomalies using Reed-Xiaoli detector

도움말 항목

Getting Started with Hyperspectral Image Processing
Basics of hyperspectral image processing.
Explore Hyperspectral Data in the Hyperspectral Viewer
This example shows how to explore hyperspectral data using the 초분광 뷰어 app.
Hyperspectral Data Correction
Describes radiometric calibration and atmospheric correction.
Spectral Indices
Describes spectral indices.
Support for Singleton Dimensions
Analysis of 1D and 2D spectral data using singleton hypercube.

추천 예제

Hyperspectral Image Analysis Using Maximum Abundance Classification

Identify different regions in a hyperspectral image by performing maximum abundance classification (MAC). An abundance map characterizes the distribution of an endmember across a hyperspectral image. Each pixel in the image is either a pure pixel or a mixed pixel. The set of abundance values obtained for each pixel represents the percentage of each endmembers present in that pixel. In this example, you will classify the pixels in a hyperspectral image by finding the maximum abundance value for each pixel and assigning it to the associated endmember class.

라이브 스크립트 열기

Classify Hyperspectral Image Using Library Signatures and SAM

Classify pixels in a hyperspectral image by using the spectral angle mapper (SAM) classification algorithm. This algorithm classifies each pixel in the test data by computing the spectral match score between the spectrum of a pixel and the pure spectral signatures read from the ECOSTRESS spectral library. This example uses a data sample from the Jasper Ridge dataset as the test data. The test data contains four endmembers latent, consisting of roads, soil, water, and trees. In this example, you will:

라이브 스크립트 열기

Classify Hyperspectral Images Using Deep Learning

Classify hyperspectral images using a custom spectral convolution neural network (CSCNN) for classification.

라이브 스크립트 열기

Classify Hyperspectral Image Using Support Vector Machine Classifier

Preprocess a hyperspectral image and classify it using a support vector machine (SVM) classifier using the Statistics and Machine Learning Toolbox™.

라이브 스크립트 열기

Endmember Material Identification Using Spectral Library

Identify the classes of endmember materials present in a hyperspectral image. The endmembers are pure spectral signature that signifies the reflectance characteristics of pixels belonging to a single surface material. The existing endmember extraction or identification algorithms extracts or identifies the pure pixels in a hyperspectral image. However, these techniques do not identify the material name or class to which the endmember spectrum belong to. In this example, you will extract the endmember signatures and then, classify or identify the class of an endmember material in the hyperspectral image by using spectral matching.

라이브 스크립트 열기

Target Detection Using Spectral Signature Matching

Detect a known target in the hyperspectral image by using spectral matching method. The pure spectral signature of the known target material is used to detect and locate the target in a hyperspectral image. In this example, you will use the spectral angle mapper (SAM) spectral matching method to detect man-made roofing materials (known target) in a hyperspectral image. The pure spectral signature of the roofing material is read from the ECOSTRESS spectral library and is used as the reference spectrum for spectral matching. The spectral signatures of all the pixels in the data cube are compared with the reference spectrum and the best matching pixel spectrum is classified as belonging to the target material.

라이브 스크립트 열기

Identify Vegetation Regions Using Interactive NDVI Thresholding

Identify the types of vegetations regions in a hyperspectral image through interactive thresholding of a normalized difference vegetation index (NDVI) map. The NDVI map of a hyperspectral dataset indicates the density of vegetation in various regions of the hyperspectral data. The NDVI value is computed using the near-infrared (NIR) and visible red (R) spectral band images from the hyperspectral data cube.

라이브 스크립트 열기

Find Regions in Spatially Referenced Multispectral Image

Identify water and vegetation regions in a Landsat 8 multispectral image and spatially reference the image using the Mapping Toolbox™.

라이브 스크립트 열기