Download Opportunities of object-based image analysis for detecting urban

Opportunities of object-based
image analysis for detecting
urban environment
Małgorzata Verőné Wojtaszek
Óbuda University/AMK, Székesfehérvár
[email protected]
IGIT 2015
Outline
Trends in Earth Observation
Urban Environment and Remote Sensing
Topics
Methods and Materials
Object-based Image Analysis
Result
Land Cover Classification
Mapping Impervious Surface
Remote sensing as a source data
Székesfehérvár in the 19th century and at present
Remote Sensing provides the most suitable systematic
approach.
New data source, data processing and analysis method
present significant progress towards automated
information extraction.
Trends in Earth Observation
Increasing data volume
Increasing data complexity
Increasing need for detailed,
up-to-date information
Data, Details, Value, Complexity
Resources
Complex Urban Scene
Abundant complex objects
o Man-made
o Natural
Spectral and geometric diversity of the object of interest
Various types of rooftops
o Material
o Shape
o Size
Shadow
o Cast by nearby objects
Urban environment and Remote Sensing
The main challenges of urban remote sensing
Spectral diversity of the object of interest
Object size to be identified (resolution)
Requested category
Urban environment and Remote Sensing
The difficulty of classification of urban
area is NOT due to the classification
algorithms
The problem is related to the
generation and selection of features
It is extremely difficult to find out the
representative training samples for
man-made objects
Spectral
similarity of road
and buildings
This is always a tough problem facing
automatic processing of urban remote
sensing images
Objectives of the study
Land cover mapping in urban area
Building extraction using elevation data
(converted from LIDAR data)
Mapping impervious surface within parcels
Urban landscape extraction using high spatial
resolution imagery and GIS techniques
Comparison of different image classification
methods in urban environment
Investigating urban sprawl through
integrating remote sensing and other
thematic maps
Objectives of the study
comparison of different image classification methods in urban
environment
to develop a methodology to generate land cover classes of
urban area from VHR satellite images
Dataset
Original bands,
NDVI, NDWI,…
Segmentation questions
input data…..
segmentation technique……
parameter…..
Szoftver: eCognitionion Developer, Idrisi, ENVI, …..
An optimal object size
Multiresolution segmentation
Scale parameter
Spectral signature
Shape, size
Compactness
Smoothness
To reach the optimum segmentation parameters
homogenous
semantically significant groups of
pixels
Land cover detection, OBIA
Categories of Urban Environments
Categories
to give the name by using accepted terminology
to use remote sensing as the primary data source
Classification System
LIDAR
Trees
grass
Hierarchical method
Remove easily identified objects first, e.g. vegetations, waters
Proceed with the left portion of image by using advanced algorithms
Building extraction: steps and results
The features used to aid the classification process
Results
Classification based on spectral and elevation features
Results / Accuracy Assessment
Geospatial Data Fusion
Point cloud
Feature
Extraction
Vector
Raster
Classification
Urban Land Cover Detection (Change
detection) with eCognition
Classification
RGB
DSM
DTM
GIS Cover and
Land
Change Detection
Source: eCognition
Image analysis workflow
Controlled with Rule Set
Segmentation
Classification
Context
Abstraction
Result
Input
Raster
Raster
Vector
Vector
Point
cloud
Point
cloud
Report
fuses raster, vector and point cloud data stacks for processing
uses pixels, objects and object networks for superior analysis
leverages context rules to achieve greater result accuracy
Source: eCognition
Mapping impervious surface
The aims
Mapping impervious surface from remote sensing
data within a parcel
Categorize parcels into classes of impervious surface
percentage
Dataset
Ortho photos (RGB + NIR, 0.5 m)
LIDAR
Parcels outlines
Mapping impervious surface
The main steps
1. Identify parcels (super object_level)
a segmentation representing the parcels
(cheesboard segmentation)
Classify the parcels (thematic layer
attributes)
2. Identify impervious surface within the
parcels (sub object_level)
A segmentation representing impervious
surface (multiresolution segmentation)
Classify impervious surface
3. Categorize parcels (classification, class
discription)
Segmentation Algorithms
Chessboard Segmentation
- creates square objects of specified size (fast, no spectral information)
- typical application: translate vector boundaries into object outlines
Segmentation Algorithms
Multiresolution Segmentation
- creates smooth representation of spectrally distinctive regions
- merges pixels with neighbors, based on homogeneity criteria
- shape and compactness parameters; layer weights
- can be applied to pixel level or existing image objects
- best results but computationally intensive
- typical applications: accurate delineation of distinct features; creating multi-scale object
hierarchy
Classification Algorithms
Assign Class
object labeling using conditions and thresholds
Fuzzy Logic Classification
combination of multiple functions in class descriptions
Mapping impervious surface
Future
How can be improved the classification in urban
environment?
Application of high resolution and accuracy satellite images and aerial
photos (0,5 m – 2 cm!)
Multiresolution detection (hyperspectral images)
Application of high resolution and accuracy Digital Surface Models (6-8
points/m2; by LIDAR and the matching of aerial image, new possibilities are
the drones!)
Taking into account cadastral map and further indexes and thematic data
Thank you for your attention