imagery in arcgis - gisworx
TRANSCRIPT
Imagery in ArcGIS
Ahmed Awady
Esri
Platform Enabling Imagery Across the Enterprise
See the Earth, Find the Patterns, Share with Others
Professional
Imagery &
Geospatial
Analysts
Server
System of
Record
System of
Insight
System of
Engagement
Image
Processing
&
Analytics
Find thePatterns
Imagery
Management
See the Earth
Imagery
Widgets & Apps
Share with Others
Management
Map
ProductionAnalysis
Content
Visualization
& Exploitation
5 Key Imagery Capabilities of ArcGIS
Content Ways to See the World
Understands 25 sensor models and 57 raster formats
Drones Drone2Map for ArcGIS
2D Image Maps (Ortho Mosaics):• Map Accurate Image Maps
• Seamless stitched for base mapping
• Digital Surface Models
3D Elevation Products:• Point Cloud (LAS)
• 3D Mesh Models
• 3D Site Models (PDF)
Smart Inspection Photos:• Non-Distorted but Map Accurate
• Works for vertical and oblique photos
• Complete 3D Measurements
Overlapping
Static Aerial Photos
Tasking
Collection
Processing
Dissemination
Tasking
Processing
Analysis
Dissemination
Drone Mapping Drone Mapping and Analysis
Collection
Collection
Online Drone Mapping
Tasking
Collection
Enterprise Drone
Mapping and Analysis
TaskingEnterprise
Server
Image
Server
AGOL
• Flight Management Software
- Multi-User Configuration
- Unlimited Elastic Processing
- Advanced 3D Visualization
- Update Feature Layers
- Web Maps & Dashboards
• Flight Management Software
- Multi-User Configuration
- Unlimited Elastic Processing
- Robust Image Management
- Raster Analytics & Analysis
- Advanced 3D Modeling
- Update Feature Layers
- Web Maps & Dash Boards
- Drone Tasking App
- PC Processing
- Imagery Analysis
- Advanced 3D Modeling
- Update Feature Layers
- Web Maps & Dashboards
- Drone Tasking App
- PC Processing
- Advanced 3D Visualization
- Update Feature Layers
- Web Maps & Dashboards
Dissemination
Analysis, Exploitation, & Dissemination
ArcGIS Drone CollectionsFour capability tiers support individual users to broad organizations
Content Imagery Management is the Cornerstone
Imagery Services
OGC & KML
Mosaic Datasets:
An Imagery
Information Model
Multiple Sensors
& Formats
Web Cataloging -
Search & Discovery
Tile Cache versus Imagery ServicesImagery
Services
Imagery
Source
Files
Imagery
Service
Pros:• Best for Analytics and Analysis (original pixel values)
• Immediately available with NO preprocessing of the imagery
• Reduces data redundancy as only the original sources are used
• Can dynamically be converted into derived products on-the-fly
Cons:• Service response can be slightly slower (measured in milliseconds)
• Not ideal for 3D visualization
Tile
Cached
Pros:• Best for Visualization / 3D Visualization
• Fastest way to serve imagery you don’t want to process/change
• Ideal for static base maps
Cons:• Must preprocess all the imagery
• Must build and store all derivative image products as separates caches
• Cannot be used for image processing or analytics
• Does not support multispectral imagery band changes
Both Approaches can be used within the same architecture
Data Model The ArcGIS Mosaic Dataset
Thousands of Images
Dynamic Scaling
In a Data Model
Seamless Mosaics
Imagery Access to Metadata
Metadata for each
image is readily
accessible to end
user clients
Data Model Dynamic Image Services
Raster Functions
Raster Function Chains
Raster Functions +Function Editor
Raster Analysis
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Imagery Tab
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• Ortho mapping
• Georeferencing
• Access to the Raster Functions Pane & Function Editor
• Image Classification
• Processing Tools
Image Classification
The things we
extract from
imagery
Set of All Features
Features
Visible
Features
Of
Interest
Features Available
to Work with
Choose your data carefullyOptimize your data if possible
• Choose your best sensor
• Optimize for seasonality and phenomenology – timing is key
• Process effectively
- Normalize for sensor anomalies
- Minimize any variation in your datasets
Autumn
Winter
SummerSpring
SegmentationPreserving the object edges
• This technique helps
- preserve edges of objects
- Provides object specific values
• A pre-processing step
Segmentation in Pro – MS Image
Segmentation – fine detail, preview
Segementation – course detail, output
Supervised Image Classification
24
Input ImageSegmenter Segmented Image
Training Samples
ClassifierClassified Image
Accuracy
assessment
Generate training
& inspect *
Mean Shift Segmentation
Maximum Likelihood
Support Vector Machine
Random Trees
Train Test/Classify.ecd
• New in Pro 2.0
Confusion matrix
Reference Image
Classified
Image
Confusion matrix
Unsupervised Image Classification
27
Input ImageSegmenter Segmented Image
Classifier Classified Image
Accuracy
assessment
ISOData
Human Labels Reclassify
Classified Image
Imagery
Visualization & Measure
Classification & Extraction
Change Detection
Raster Analytics Cloud Processing
Elastic Processing in Scalable Environments
Image Processing – Raster Analytics
What is Raster Analytics?
• ArcGIS has a new way to create and execute spatial
analysis models and image processing chains which
leverage distributed storage and analytics
- Raster Analytics works with your existing GIS data and imagery
- register your data and go, importing existing data to distributed storage is not mandatory
- Raster Analytics can optimize your data for distributed analytics
- import your data into ArcGIS distributed storage which further improves the scalability of distributed analytics
- Raster Analytics is designed to scale with your organization’s demands
- scale up to get the job done, scale down when resources are no longer needed
Solve New Problems with Raster Analytics
• run models against data that is too big for single desktop
- small and medium scale global rasters (big geography)
- large scale local or regional rasters (high resolution)
• run models against massive collections and scale it
• run models and meet time constraints
months weeks days hours minutes
Raster Analytics is Powerful
• run a model based on a single function
• run a model by combining many functions
Math
Abs
Arithmetic
Band
Arithmetic
Calculator
Divide
Exp
Exp10
Exp2
Float
Int
Ln
Log10
Log2
Minus
Mod
Negate
Plus
Power
Round Down
Round Up
Square
Square Root
Times
Bitwise And
Bitwise Left
Shift
Bitwise Not
Bitwise Or
Bitwise Right
Shift
Bitwise Xor
Boolean And
BooleanNot
Boolean Or
Boolean Xor
Equal To
Greater Than
Greater Than
Equal
Is Null
Less Than
Less Than
Equal
Not Equal
ArgStatistics
Cell Statistics
Statistics
ACos
ACosH
ASin
ASinH
ATan
ATan2
ATanH
Cos
CosH
Sin
SinH
Tan
TanH
Data Management & Conversion
Raster to Vector
Vector to Raster
Colormap
Colormap To RGB
Complex
Grayscale
Remap / Reclass
Spectral Conversion
Unit Conversion
Vector Field
LAS to Raster
LAS Dataset to Raster
Clip
Composite
Extract Bands
Mask
Mosaic Rasters
Rasterize Features
Reproject
Interpolation
Interpolate Irregular Data
Nearest Neighbor
IDW
EBK
Swath
Correction
Apparent Reflectance
Geometric Correction
Speckle Filtering (Lee,Frost,Kuan)
Analysis: Image Segmentation & Classification
Segmentation (Mean Shift)
Training (ISO, SVM, ML)
Supervised Classification
Visualization & Appearance
Contrast and Brightness
Convolution
Pansharpening
Resample
Statistics and Histogram
Stretch
Surface Generation & Analysis
Aspect
Curvature
Elevation Void Fill
Hillshade
Shaded Relief
Slope
Viewshed
Analysis: Overlay
Weighted Sum
Weighted Overlay
Analysis: Band Math & Indices
NDVI / NDVI Colorized
SAVI / MSAVI / TSAVI
GEMI
GVI (Landsat TM)
PVI
Tasseled Cap (Kauth-Thomas)
Binary Thresholding
Analysis: Distance & Density
Euclidean Distance
Cost Distance
Cost Path
Kernel Density
Analysis: Zonal
Zonal Statistics
Zonal Histogram
Zonal Geometry
Python
Custom Algorithms
Conditionals
Con
Set Null
Raster Analytics is Easy
• easy to get started, it is “out of the box analytics”
- install on nodes -> start Raster Analytic services -> go
• ArcGIS Pro user experience
- just works with layers
- visual modeler to design simple and complex models
• results are immediately available in your Web GIS
- no publishing workflow required
Raster Analytics Conceptual Overview
ArcGIS
Enterprise
Web GIS Layers
GIS Data & Imagery
import and optimize
(optional)
New Web GIS Layers
distributed raster analytics cluster
ArcGIS Pro
(Users, Analysts, Researchers)
Design & Run Model
Model Execution Distribution
Developers & System Integration
Raster Analytics can power systems that need to
execute spatial analysis and image processing
models in a distributed and scalable environment.
It is designed for users, developers, and system
integrators.
Results are stored in distributed
storage and are immediately
available as new Web GIS Layers
which are already optimized for
further analytics
distributed raster datastore
GdbFilesWCS ServicesArcGIS Services
analysis results as a new Web GIS Layers
Web UX
ArcGIS 10.7Raster Analytics Test Cases
Raster Analytics Test Case: Terrain Suitability
Global SRTM 90m
0
100
200
300
400
500
600
700
800
1 2 4 8 16
790
425
252
126
80
Min
ute
s
Raster Analytics Processorsesri virtual machine
• 16GB RAM, 8 cores, NAS storage
13.12 hours
80 minutes
terrain suitability model• compute slope
• compute aspect
• remap
• overlay
global terrain suitability raster
Raster Analytics Test Case: Landsat Processing
(foreach) input scene
top of atmosphere
correction
modified soil adjusted
vegetation index
remap to classes
mask
no data
output thematic raster
Distributed Raster Analytics Cluster
• single node
• AWS c3.8xlarge
• 60GB RAM, 32 cores, 500GB SSD
• 200 Raster Analytics Processors
Infrastructure ProcessingInput Collection Output
Landsat GLS 1990
• 7422 Multispectral Scenes
Thematic Rasters
• 7422 Thematic Rasters
• Distributed Raster Datastore
2 hours 48 minutes44 scenes per minute
¾ scene per second
Thank you!