{"id":2962544,"date":"2026-05-14T11:43:03","date_gmt":"2026-05-14T18:43:03","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2962544"},"modified":"2026-05-14T18:32:01","modified_gmt":"2026-05-15T01:32:01","slug":"new-hyperspectral-analysis","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis","title":{"rendered":"ArcGIS Pro 3.7: New Hyperspectral Imagery Tools for GIS and Image Analysis"},"author":10352,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,22851,22931],"tags":[774652,432762,781061,780841,773212],"industry":[],"product":[36561],"class_list":["post-2962544","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-national-government","category-imagery","tag-hyperspectral","tag-image-analyst","tag-spectral-analysis","tag-spectral-library","tag-stac","product-arcgis-pro"],"acf":{"show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/small_banner.png","wide_image":false,"authors":[{"ID":10352,"user_firstname":"Hong","user_lastname":"Xu","nickname":"Hong Xu","user_nicename":"hxu","display_name":"Hong Xu","user_email":"hxu@esri.com","user_url":"","user_registered":"2019-12-20 16:44:39","user_description":"Hong Xu is a Principal Software Product Engineer on Esri\u2019s imagery team, where she has been contributing since 1999. Her work focuses on advancing analytical methods for Earth observation data, including image time series (image cubes), hyperspectral imagery, multidimensional raster analysis, and altimetry data. She is passionate about bridging scientific research and software development to help users better understand environmental change through geospatial analysis.","user_avatar":""},{"ID":325562,"user_firstname":"Shengan","user_lastname":"Zhan","nickname":"Shengan Zhan","user_nicename":"szhan","display_name":"Shengan Zhan","user_email":"szhan@esri.com","user_url":"","user_registered":"2023-01-18 19:57:27","user_description":"Shengan is a senior product engineer on the raster team. He joined ESRI since 2020 and his focus is on multidimensional image analysis.","user_avatar":""},{"ID":5991,"user_firstname":"Jeff","user_lastname":"Liedtke","nickname":"Jeff Liedtke","user_nicename":"jliedtke","display_name":"Jeff Liedtke","user_email":"JLiedtke@esri.com","user_url":"","user_registered":"2018-03-02 00:17:51","user_description":"Jeff Liedtke is a PE and Documentation Lead for the Raster Team at Esri. He has a background in remote sensing, photogrammetry and image processing. Applying remote sensing techniques to provide valuable information for operational decision support applications is his passion.","user_avatar":""}],"short_description":"Explore new hyperspectral imagery tools in ArcGIS Pro 3.7, including spectral analysis, band reduction, and streamlined GIS workflows.","flexible_content":[{"acf_fc_layout":"content","content":"

Advances in remote sensing have enabled global, multi-decadal observation of the Earth. Today, that capability extends further into the spectral dimension with hyperspectral imagery, which captures detailed material characteristics through their spectral signatures across dense, continuous spectral bands.\u00a0 For GIS and image analysts, this means more precise material identification, improved classification, and deeper insight into land surface processes.<\/p>\n

Building on the major hyperspectral release in 2025, ArcGIS Pro 3.7 introduces a set of enhancements designed to make these capabilities more accessible and efficient in day-to-day workflows. From easier data access to improved spectral analysis tools, this release focuses on helping you move more quickly from raw data to meaningful results.<\/p>\n"},{"acf_fc_layout":"content","content":"

Explore Spectral Features More Effectively<\/h2>\n

Continuum Removal in the Spectral Signature Viewer<\/h3>\n

Understanding subtle spectral characteristics is critical for tasks like mineral identification or vegetation analysis. The Spectral Signature Viewer now includes continuum removal, a technique that normalizes spectra and highlights absorption features.<\/p>\n

Previously available only within geoprocessing tools, this capability is now part of your interactive analysis workflow.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962551,"id":2962551,"title":"fig3_continuum","filename":"fig3_continuum.jpg","filesize":22973,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig3_continuum","alt":"continuum removal tool","author":"10352","description":"","caption":"","name":"fig3_continuum","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:42:08","modified":"2026-04-10 23:42:27","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":316,"height":248,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","medium-width":316,"medium-height":248,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","medium_large-width":316,"medium_large-height":248,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","large-width":316,"large-height":248,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","1536x1536-width":316,"1536x1536-height":248,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","2048x2048-width":316,"2048x2048-height":248,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","card_image-width":316,"card_image-height":248,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig3_continuum.jpg","wide_image-width":316,"wide_image-height":248}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

In the Spectral Signature Pane<\/strong>, highlight the material and click the Remove Continuum<\/strong> tool, the spectral signature with the continuum removed will be added to the pane and viewer. In practice, this allows you to quickly inspect and analyze the absorption features.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962552,"id":2962552,"title":"fig4_continuum_signatures","filename":"fig4_continuum_signatures.jpg","filesize":47531,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig4_continuum_signatures","alt":"continuum removed signature","author":"10352","description":"","caption":"","name":"fig4_continuum_signatures","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:42:50","modified":"2026-04-10 23:43:15","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":550,"height":405,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","medium-width":354,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","medium_large-width":550,"medium_large-height":405,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","large-width":550,"large-height":405,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","1536x1536-width":550,"1536x1536-height":405,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","2048x2048-width":550,"2048x2048-height":405,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","card_image-width":550,"card_image-height":405,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig4_continuum_signatures.jpg","wide_image-width":550,"wide_image-height":405}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Calculate Mean Spectra<\/h3>\n

When working with multiple samples of the same material or land cover type, generating a representative signature is often necessary. The new Calculate Mean<\/strong> tool lets you quickly average selected spectra and add the result back into your analysis.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962553,"id":2962553,"title":"fig5_mean","filename":"fig5_mean.jpg","filesize":22325,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig5_mean","alt":"Calculate mean signature","author":"10352","description":"","caption":"","name":"fig5_mean","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:45:51","modified":"2026-04-10 23:46:09","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":300,"height":244,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","medium-width":300,"medium-height":244,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","medium_large-width":300,"medium_large-height":244,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","large-width":300,"large-height":244,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","1536x1536-width":300,"1536x1536-height":244,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","2048x2048-width":300,"2048x2048-height":244,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","card_image-width":300,"card_image-height":244,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig5_mean.jpg","wide_image-width":300,"wide_image-height":244}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

To use, highlight the signatures and click the Calculate Mean<\/strong> tool, where the calculated signature will be added to the viewer and pane. This is especially useful for creating cleaner inputs for classification, spectral unmixing, or target detection workflows.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962554,"id":2962554,"title":"fig6_mean_signature","filename":"fig6_mean_signature.jpg","filesize":35709,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig6_mean_signature","alt":"Mean signatures","author":"10352","description":"","caption":"","name":"fig6_mean_signature","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:46:38","modified":"2026-04-10 23:46:49","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":550,"height":259,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","medium-width":464,"medium-height":219,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","medium_large-width":550,"medium_large-height":259,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","large-width":550,"large-height":259,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","1536x1536-width":550,"1536x1536-height":259,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","2048x2048-width":550,"2048x2048-height":259,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","card_image-width":550,"card_image-height":259,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig6_mean_signature.jpg","wide_image-width":550,"wide_image-height":259}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Reduce Data Complexity Without Losing Insight<\/h2>\n

Hyperspectral datasets are information-rich\u2014but also computationally demanding. ArcGIS Pro 3.7 introduces the Reduce Spectral Bands<\/strong> tool to help you manage this complexity.\u00a0It supports two commonly used approaches:<\/p>\n

    \n
  • PCA (Principal Component Analysis) for general dimensionality reduction<\/li>\n
  • MNF (Minimum Noise Fraction) for noise reduction and improved signal separation<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":2962564,"id":2962564,"title":"fig77_reduction_tool","filename":"fig77_reduction_tool.jpg","filesize":43791,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig77_reduction_tool","alt":"MNF and PCA tool","author":"10352","description":"","caption":"","name":"fig77_reduction_tool","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:40:21","modified":"2026-04-11 00:40:44","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":560,"height":358,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","medium-width":408,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","medium_large-width":560,"medium_large-height":358,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","large-width":560,"large-height":358,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","1536x1536-width":560,"1536x1536-height":358,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","2048x2048-width":560,"2048x2048-height":358,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","card_image-width":560,"card_image-height":358,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig77_reduction_tool.jpg","wide_image-width":560,"wide_image-height":358}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    A typical workflow that involves reducing a high dimensional dataset is classification. After applying MNF or PCA, the resulting components can be used as input to machine learning or deep learning-based classification workflows.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962557,"id":2962557,"title":"fig8_classification","filename":"fig8_classification.jpg","filesize":89257,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig8_classification","alt":"AVIRIS hyperspectral image classification","author":"10352","description":"","caption":"","name":"fig8_classification","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:15:10","modified":"2026-04-11 00:15:29","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":550,"height":388,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","medium-width":370,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","medium_large-width":550,"medium_large-height":388,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","large-width":550,"large-height":388,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","1536x1536-width":550,"1536x1536-height":388,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","2048x2048-width":550,"2048x2048-height":388,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","card_image-width":550,"card_image-height":388,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig8_classification.jpg","wide_image-width":550,"wide_image-height":388}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Derive Spectral Signatures Directly from Imagery<\/h2>\n

    Spectral signatures used in analysis may come from libraries such as the USGS spectral library or they can be collected in the field.<\/p>\n

    The new Extract Spectra From Image<\/strong> tool allows you to generate spectral libraries directly from your imagery using field observations. You can input point or polygon feature classes repressing known materials- such as tree species or land cover types and extract their corresponding spectral signatures.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962546,"id":2962546,"title":"extract_spectra","filename":"extract_spectra.jpg","filesize":30614,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/extract_spectra","alt":"","author":"10352","description":"","caption":"","name":"extract_spectra","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:13:08","modified":"2026-04-10 23:13:38","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":300,"height":307,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","medium-width":255,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","medium_large-width":300,"medium_large-height":307,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","large-width":300,"large-height":307,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","1536x1536-width":300,"1536x1536-height":307,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","2048x2048-width":300,"2048x2048-height":307,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","card_image-width":300,"card_image-height":307,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/extract_spectra.jpg","wide_image-width":300,"wide_image-height":307}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    In a vegetation analysis scenario, field points representing trees, shrubs, and grassland are used to extract class-specific spectra.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962559,"id":2962559,"title":"fig10_samples","filename":"fig10_samples.jpg","filesize":20608,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig10_samples","alt":"calculated signatures","author":"10352","description":"","caption":"","name":"fig10_samples","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:23:22","modified":"2026-04-11 00:23:32","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":452,"height":182,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples-213x182.jpg","thumbnail-width":213,"thumbnail-height":182,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","medium-width":452,"medium-height":182,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","medium_large-width":452,"medium_large-height":182,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","large-width":452,"large-height":182,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","1536x1536-width":452,"1536x1536-height":182,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","2048x2048-width":452,"2048x2048-height":182,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","card_image-width":452,"card_image-height":182,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig10_samples.jpg","wide_image-width":452,"wide_image-height":182}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    These signatures can then feed directly into tools like Linear Spectral Unmixing<\/strong> to generate abundance maps for each vegetation type. This creates a seamless workflow from field data to analytical output.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962567,"id":2962567,"title":"f1111_unmixing","filename":"f1111_unmixing.png","filesize":39236,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/f1111_unmixing","alt":"unmixing result","author":"10352","description":"","caption":"","name":"f1111_unmixing","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:57:05","modified":"2026-04-11 00:57:14","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":312,"height":314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","medium-width":259,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","medium_large-width":312,"medium_large-height":314,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","large-width":312,"large-height":314,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","1536x1536-width":312,"1536x1536-height":314,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","2048x2048-width":312,"2048x2048-height":314,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","card_image-width":312,"card_image-height":314,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/f1111_unmixing.png","wide_image-width":312,"wide_image-height":314}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Simplified Spectral Index Calculation with Wavelengths<\/h2>\n

    Spectral indices are widely used in analysis but applying them to hyperspectral data can be challenging due to inconsistent band naming across sensors.<\/p>\n

    ArcGIS Pro 3.7 addresses this challenge with wavelength-enabled expressions in the Band Arithmetic<\/strong> raster function.\u00a0 For example, the Plant Senescence Reflectance Index (PSRI): (n678.0\u2212n500.0)\/n750.0, where n<\/em> represents wavelength in nanometers, can be input directly into the Band Arithmetic function.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962561,"id":2962561,"title":"indices","filename":"indices.jpg","filesize":13562,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/indices","alt":"indices calculation","author":"10352","description":"","caption":"","name":"indices","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:24:41","modified":"2026-04-11 00:25:09","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":250,"height":214,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","medium-width":250,"medium-height":214,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","medium_large-width":250,"medium_large-height":214,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","large-width":250,"large-height":214,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","1536x1536-width":250,"1536x1536-height":214,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","2048x2048-width":250,"2048x2048-height":214,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","card_image-width":250,"card_image-height":214,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/indices.jpg","wide_image-width":250,"wide_image-height":214}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    ArcGIS Pro automatically selects the closest matching bands, eliminating the need to manually identify band indices. Below is the output of PSRI.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962562,"id":2962562,"title":"fig13_indice_map","filename":"fig13_indice_map.jpg","filesize":82737,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig13_indice_map","alt":"PSRI map","author":"10352","description":"","caption":"","name":"fig13_indice_map","status":"inherit","uploaded_to":2962544,"date":"2026-04-11 00:28:25","modified":"2026-04-11 00:28:38","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":366,"height":326,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","medium-width":293,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","medium_large-width":366,"medium_large-height":326,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","large-width":366,"large-height":326,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","1536x1536-width":366,"1536x1536-height":326,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","2048x2048-width":366,"2048x2048-height":326,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","card_image-width":366,"card_image-height":326,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig13_indice_map.jpg","wide_image-width":366,"wide_image-height":326}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Expanded Support for Hyperspectral data<\/h2>\n

    New Tanager and Dragonette raster types<\/h3>\n

    ArcGIS Pro 3.7 expands support for hyperspectral data with new raster types for Tanager and Dragonette imagery from Planet and Wyvern data providers.<\/p>\n

    For analysts, this means less time spent on data preparation. You can directly load a hyperspectral image product and the associated products from a single HDF5 file using predefined templates or build mosaic datasets to manage multiple scenes across a study area.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962548,"id":2962548,"title":"fig1_product","filename":"fig1_product.jpg","filesize":30507,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig1_product","alt":"Hyperspectral image product","author":"10352","description":"","caption":"","name":"fig1_product","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:31:37","modified":"2026-04-10 23:31:51","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":450,"height":231,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","medium-width":450,"medium-height":231,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","medium_large-width":450,"medium_large-height":231,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","large-width":450,"large-height":231,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","1536x1536-width":450,"1536x1536-height":231,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","2048x2048-width":450,"2048x2048-height":231,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","card_image-width":450,"card_image-height":231,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig1_product.jpg","wide_image-width":450,"wide_image-height":231}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Direct Access to Hyperspectral Data STAC Catalogs<\/strong><\/h3>\n

    Many hyperspectral datasets are now distributed through STAC catalogs. With ArcGIS Pro 3.7, you can directly connect to Tanager and Wyvern STAC catalogs and start working with the data immediately. This makes it easier to discover datasets and quickly evaluate spectral signatures.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2962549,"id":2962549,"title":"fig2_STAC","filename":"fig2_STAC.jpg","filesize":42025,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\/fig2_stac","alt":"access HSI STAC catalog","author":"10352","description":"","caption":"","name":"fig2_stac","status":"inherit","uploaded_to":2962544,"date":"2026-04-10 23:32:19","modified":"2026-04-10 23:32:40","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":500,"height":330,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","medium-width":395,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","medium_large-width":500,"medium_large-height":330,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","large-width":500,"large-height":330,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","1536x1536-width":500,"1536x1536-height":330,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","2048x2048-width":500,"2048x2048-height":330,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","card_image-width":500,"card_image-height":330,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/fig2_STAC.jpg","wide_image-width":500,"wide_image-height":330}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Summary<\/h2>\n

    Hyperspectral imagery provides an unprecedented level of detail about the Earth\u2019s surface. With ArcGIS Pro 3.7, new tools and enhancements make it easier than ever to extract meaningful information and turn complex spectral data into actionable insights.<\/p>\n

    We\u2019d love to hear how you\u2019re using these new capabilities in your workflows.<\/p>\n"}],"related_articles":[{"ID":2942951,"post_author":"10352","post_date":"2025-10-26 23:51:36","post_date_gmt":"2025-10-27 06:51:36","post_content":"","post_title":"Empower Hyperspectral Image Analysis with Spectral Library Support - New Spectral Analysis Tools in ArcGIS Pro 3.6\u00a0","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"new-spectral-analysis-tools","to_ping":"","pinged":"","post_modified":"2025-11-13 07:38:37","post_modified_gmt":"2025-11-13 15:38:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2942951","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"1","filter":"raw"},{"ID":2554012,"post_author":"10352","post_date":"2024-11-11 11:57:03","post_date_gmt":"2024-11-11 19:57:03","post_content":"","post_title":"Map Oaks Using AVIRIS Hyperspectral Imagery","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"map-oaks","to_ping":"","pinged":"","post_modified":"2024-11-12 09:59:04","post_modified_gmt":"2024-11-12 17:59:04","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2554012","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":2332262,"post_author":"10352","post_date":"2024-05-09 11:22:30","post_date_gmt":"2024-05-09 18:22:30","post_content":"","post_title":"Working with EMIT Hyperspectral Imagery in ArcGIS","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"working-with-emit-data-in-arcgis","to_ping":"","pinged":"","post_modified":"2024-05-13 09:19:34","post_modified_gmt":"2024-05-13 16:19:34","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2332262","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"2","filter":"raw"}]},"yoast_head":"\nNew hyperspectral imagery tools in ArcGIS Pro 3.7<\/title>\n<meta name=\"description\" content=\"Explore new hyperspectral imagery tools in ArcGIS Pro 3.7, including spectral analysis, band reduction, and streamlined GIS workflows.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ArcGIS Pro 3.7: New Hyperspectral Imagery Tools for GIS and Image Analysis\" \/>\n<meta property=\"og:description\" content=\"Explore new hyperspectral imagery tools in ArcGIS Pro 3.7, including spectral analysis, band reduction, and streamlined GIS workflows.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/new-hyperspectral-analysis\" \/>\n<meta property=\"og:site_name\" content=\"ArcGIS Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/esrigis\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-15T01:32:01+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@ESRI\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" 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