초분광 영상 처리
Hyperspectral Imaging Library for Image Processing Toolbox™는 초분광 영상 처리와 다중분광 영상 처리 및 그 시각화를 위한 MATLAB® 함수와 툴을 제공합니다.
이 라이브러리의 함수를 사용하면 초분광 영상 센서 및 다중분광 영상 센서를 사용하여 다양한 파일 형식으로 캡처한 데이터를 읽고, 쓰고, 처리할 수 있습니다. 라이브러리는 NITF(National Imagery Transmission Format ), ENVI(Environment for Visualizing Images), TIFF(Tagged Image File Format), MTL(Metadata Text Extension), HDF(Hierarchical Data Format) 및 SAFE(Standard Archive Format for Europe) 파일 형식을 지원합니다.
이 라이브러리는 대역 선택, 잡음 제거, 복사 보정 및 대기 보정, 차원 축소, 엔드멤버 추출, 구성 비율 맵(abundance map) 추정, 스펙트럼 매칭, 이상 감지, 스펙트럼 인덱스 계산 및 분할을 위한 알고리즘을 제공합니다.
초분광 뷰어 앱을 사용하여 초분광 데이터와 다중분광 데이터를 읽어 들이고, 메타데이터와 지리공간 정보를 표시하고, 개별 대역 영상과 그 히스토그램을 시각화하고, 데이터 큐브의 픽셀 또는 영역에 대해 스펙트럼 플롯을 만들고, 엔드멤버를 플로팅하고, 다양한 색상 또는 가색상(false color) 표현을 생성하고, 스펙트럼 인덱스를 계산하고 결과를 내보낼 수 있습니다.
초분광 영상 분석 및 다중분광 영상 분석을 수행하려면 애드온 탐색기에서 Hyperspectral Imaging Library for Image Processing Toolbox를 다운로드하십시오. 애드온 다운로드에 대한 자세한 내용은 애드온을 받고 관리하기를 참조하십시오.
앱
| 초분광 뷰어 | 초분광 및 다중분광 데이터 시각화 |
함수
도움말 항목
시작하기
- Get Started with Hyperspectral Image Processing
Basics of hyperspectral image processing. - Analyze Hyperspectral and Multispectral Images
Describes approaches to hyperspectral and multispectral imaging. - Explore Hyperspectral and Multispectral Data in the Hyperspectral Viewer
This example shows how to explore hyperspectral and multispectral data using the 초분광 뷰어 app. - Process Large Hyperspectral and Multispectral Images
This example shows how to process small regions of large hyperspectral and multispectral images. - Hyperspectral and Multispectral Data Correction
Describes radiometric calibration, atmospheric correction, and spectral correction. - Spectral Matching and Target Detection Techniques
Techniques for target detection and spectral matching. - Spectral Indices
Describes spectral indices. - Support for Singleton Dimensions
Analysis of 1-D and 2-D spectral data using singleton hypercube.
분류
- Classify Hyperspectral Image Using Library Signatures and SAM
Classify pixels in a hyperspectral image by using the spectral angle mapper (SAM) classification algorithm. - Classify Hyperspectral Images Using Deep Learning
This example shows how to perform hyperspectral image classification using a custom spectral convolution neural network (CSCNN). - Classify Hyperspectral Image Using Support Vector Machine Classifier
This example shows how to perform hyperspectral image classification using a support vector machine (SVM) classifier.
영역 식별
- Target Detection Using Spectral Signature Matching
Detect a known target in the hyperspectral image by using the spectral matching method. - 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. - Find Regions in Spatially Referenced Multispectral Image
This example shows how to identify water and vegetation regions in a Landsat 8 multispectral image and spatially reference the image.
디지털 트윈
- Generate RoadRunner Scene Using Aerial Hyperspectral and Lidar Data (Automated Driving Toolbox)
Generate RoadRunner scene from aerial hyperspectral and lidar data.
분할
- Interactively Segment Hyperspectral Image Using Segment Anything Model
This example shows how to interactively preprocess and segment a hyperspectral image using the Segment Anything Model (SAM). (R2025a 이후)
추천 예제
Predict Sugar Content in Grape Berries Using PLS Regression on Hyperspectral Data
Perform non-destructive testing (NDT) using hyperspectral data to predict sugar content in grape berries.
Classify Land Cover Using Hyperspectral and Lidar Data
Classify land cover using a fusion of hyperspectral and lidar data.
Segment Spleen in Hyperspectral Image of Porcine Tissue
Segment a spleen in a hyperspectral image of porcine tissue.
Classify Land Cover Using Sentinel-2 Multispectral Images
Fine-tune a pretrained segmentation model backbone, using Sentinel-2 Level 2A multispectral images and CORINE Land Cover 2018 data, for pixel-level land-cover classification.
Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection
Detect ships from Sentinel-1 C Band SAR Data using YOLOX object detection.
Map Flood Areas Using Sentinel-1 SAR Imagery
Map flooded areas using Sentinel-1 Synthetic Aperture Radar (SAR) images.
Change Detection in Hyperspectral Images
Detect changes in land cover from hyperspectral images taken at different times.
Manually Label ROIs in Multispectral Image
Manually select regions of interest (ROIs) from a multispectral image and save them in a shapefile.
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.
Endmember Material Identification Using Spectral Library
Identify the classes of endmember materials present in a hyperspectral image.
Automate Pixel Labeling of Hyperspectral Images Using Image Labeler
Load hyperspectral images into the 영상 레이블 지정기 (Computer Vision Toolbox) and automatically label pixels.
