sparse representation-based analysis of hyperspectral...
TRANSCRIPT
1
Sparse Representation-based Analysis of Hyperspectral Remote Sensing Data
Ribana Roscher
Institute of Geodesy and Geoinformation
Remote Sensing Group, University of Bonn
2
Remote Sensing Image Data
Infrared-Red-Green
Remote Sensing: Observing something from a distance without physical contact
3
Remote Sensing Image Data
altigator.com, modified
4
Hyperspectral Image Data
wavelength [μm]
reflecta
nce
5
Remote Sensing Tasks
Unmixing
Anomaly detection
Classification
Sparse representation-based analysis
6
Sparse Representation
7
Unmixing
8
Unmixing TaskProcessed satellite image
Pixel with class information (labeled)
Pixel without class information (unlabeled)
Endmember extraction
Manually or
Automatically
Reconstruction by sparse representation
Evaluation
Sub-pixel quantification
?
9
Unmixing Task
1. task: Find suitable endmembers
manually derived spectral library
archetypal dictionary
2. task: Estimate fractions (activations)
Sparse representation
10
Archetypal Analysis: SiVM
Archetypal analysis finds the extreme points (archetypes) in feature space
Efficient determination by Simplex Volume Maximization (SiVM)
Assumption: Convex hull consists of points, which maximize the volume
Christian Thurau, Kristian Kersting, Christian Bauckhage (2010): Yes We Can – Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization
11
Archetypal Analysis: SiVM
1. Randomly choose (virtual) starting point
2. Choose sample which is farthest away
3. Set this sample as first archetype
4. Choose next sample which is farthest away from all previous archetypes
Christian Thurau, Kristian Kersting, Christian Bauckhage (2010): Yes We Can – Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization
12
Data
Berlin-Urban-Gradient dataset 2009
http://dataservices.gfz-potsdam.de/enmap/showshort.php?id=escidoc:1823890&show_gcmdcitation=false
13
Data
http://dataservices.gfz-potsdam.de/enmap/showshort.php?id=escidoc:1823890&show_gcmdcitation=false
Study site: Southwest of Berlin
Hyperspectral image
Manually derived spectral library
Reference land cover information
Simulated EnMAP scene
14
Hyperspectral Data
Airborne Sensor: HyMap
111 spectral bands
Observed wavelength 450nm – 2500nm
Spatial resolution of 3.6m
Visualized as RGB-image with the wavelengths R=640nm, G=540nm and B=450nm
15
Reference Information
Reference information was manually obtained
digital orthophotos
cadastral data
4 land cover classes
Impervious surface
Vegetation
Soil & Sand
Water
16
Simulated EnMAP Data
Task: Reconstruction of fractions of simulated EnMAP data
Simulated EnMAP scene of the same area
Spatial resolution of 30m
1495 EnMAP pixels were obtained from the simulation tool, containing the fractions of the land cover classes ranging from 0 to 100%
17
Archetypal dictionaries were interpreted using reference data
High total amount of spectra in the manually derived spectral library
Archetypal Dictionary vs. Manually Derived Spectral Library
Archetypaldictionary
Manually derived library
Imp. Surface 25 39
Vegetation 12 31
Soil 2 4
Water 1 1
∑ 40 75
18
Evaluation
High number of elementary spectra in library results in a small reconstruction error
All dictionaries achieve similar and satisfactory solutions
Archetypaldictionary
Manually derived library
1.1 0.0
MA
E [
%]
Imp. Surface 12.2 16.0
Vegetation 11.0 9.2
Soil 2.5 2.1
Water 1.9 12.2
Ø 6.8 9.9
Drees, L. and Roscher, R. (2017). Archetypal analysis for sparse representation-based hyperspectral sub-pixel quantification, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 133-139
19
Anomaly Detection
20
Anomaly Detection Task
Dataset
• Hyperspectral images (healthy and infected plants)
• 3D pointclouds
• No label information
Dictionary elements extraction
Sparse representation
• Standard dictionary
• Mixed topographic dictionary
Evaluation
Detected symptoms
21
Biological Material
Sugar beet plants (cultivar Pauletta; KWS GmbH, Einbeck, Germany) partially infected by the plant pathogen Cercospora beticola
22
Hyperspectral 3D Data
Hyperspectral pushbroom sensor with 1600 pixel observing a spectral signature from 400nm to 1000nm
Perceptron laser triangulation scannerHyperspectral image
Inclination map
Depth map
23
Hyperspectral 3D Data
Characteristic of hyperspectral data obtained from a healthy plant
24
Hyperspectral 3D Data
• Arrow direction: Difference of two wavelengths to reference signature (black dot)
• Arrow length: Value of difference
• Color (HSV): Angle and value of difference
25
Mixed Topographic Dictionaries
Integration of prior knowledge into dictionary by means of inclination and depth groups
Learned from healthy plant pixels
Optimization with Group Orthogonal Matching Pursuit
26
Results
Blue: specular reflections
Green: leaf veins
Orange: disease symptoms
27
Results
Standard dictionary
Mixed topographic dictionary
Specular reflections Leaf veins Disease symptomsRoscher, R., Behmann, J., Mahlein, A.-K., & Plümer, L. (2016). On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models, Workshop on Hyperspectral Image and Signal Processing.
28
Results
Original image Reconstruction error index with standard dictionary
Recontruction error index with mixed topographic dictionary
Roscher, R., Behmann, J., Mahlein, A.-K., & Plümer, L. (2016). On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models, Workshop on Hyperspectral Image and Signal Processing.
29
Classification with Self-taught Learning
30
Classification TaskProcessed satellite images
Pixel with class information (labeled)
Pixel without class information (unlabeled)
Feature learning
Classification
Learning step
Testing step
Evaluation + Post-processing
Land use and land cover map
Land use and land cover map Posterior
probabilities
31
Self-taught Learning
Dictionary contains unlabeled data
Assumption: samples of the same class are reconstructed with a similar set of dictionary elements and similar weights
Goal: new representation is highly discriminative
training
32
Feature/Representation Learning
Learning a new data representation which is more suitable for a given task than the original data representation
Powerful feature representation Discriminative Robust Lower complexity Easier to interpret
33
What is a Good Representation?
infrared
greenclass 1
class 2
feature 2
feature 1 class 1
class 2
Original representation Discriminative representation
34
Self-taught Learning
Dictionary is fixed
Classifier is trained and tested with new representation
testing
35
Self-taught Learning for Land Cover Classification
Landsat 5 TM image near Novo Progresso (Brazil)
Ca. 8000x8000 pixel
30x30m spatial resolution
Area characterized by fire clearing
Reference information: Forest, deforestation (fire clearing) and arable land
Subarea: ~900km2
36
Dictionary Choice
~1 Mio. image patches
Archetypal analysis to decide on most important ones
37
Self-taught Learning for Land Cover Classification
Roscher, R., Römer, C., Waske, B., Plümer, L. (2015). Landcover Classification with Self-taught Learning on Archetypal Dictionaries, IGARSS, Symposium Paper Prize Award
Satellite image Land cover Maximum posterior probability (certainty)
Posterior probability: arable
38
Summary
Sparse representation is a versatile tool
Exploitation of unlabeled samples for learning
Self-taught learning
Unsupervised learning (such as anomaly detection)
More and more research goes into the direction of unsupervised pre-training in combination with supervised learning