cloud computing & big data - morris ... outline of the course 1. cloud computing & big data 2....

Dr. Gabriele Cavallaro Postdoctoral Researcher High Productivity Data Processing Group Juelich Supercomputing Centre, Germany

Remote Sensing Systems and Applications (1)

October 16th, 2018 Room V02 – 138

Cloud Computing & Big Data PARALLEL & SCALABLE MACHINE LEARNING & DEEP LEARNING

PRACTICAL LECTURE 6.1

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Outline

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Outline of the Course

1. Cloud Computing & Big Data

2. Machine Learning Models in Clouds

3. Apache Spark for Cloud Applications

4. Virtualization & Data Center Design

5. Map-Reduce Computing Paradigm

6. Deep Learning driven by Big Data

7. Deep Learning Applications in Clouds

8. Infrastructure-As-A-Service (IAAS)

9. Platform-As-A-Service (PAAS)

10. Software-As-A-Service (SAAS)

11. Data Analytics & Cloud Data Mining

12. Docker & Container Management

13. OpenStack Cloud Operating System

14. Online Social Networking & Graphs

15. Data Streaming Tools & Applications

16. Epilogue

+ additional practical lectures for our

hands-on exercises in context

 Practical Topics

 Theoretical / Conceptual Topics

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Outline

 Remote Sensing Background

 Data Acquisition

 Preprocessing

 Feature Extraction and Selection

 Copernicus: Sentinel 2 Mission

Practical Lecture 6.1 – Remote Sensing Systems and Applications

 The term remote sensing was first used in the United States in the 1950s by Ms. Evelyn Pruitt of the U.S. Office of Naval Research

Remote (without physical contact) Sensing (measurement of information)

• Measurement of radiation of different wavelengths reflected or emitted from distant objects or materials

• They may be categorized by class/type, substance, and spatial distribution

[1] Satellite (1960)

Remote Sensing

[2] The Earth-Atmosphere Energy Balance Practical Lecture 6.1 – Remote Sensing Systems and Applications

• Suitable for many applications

• Non-invasive method

• Satellite platforms • Invaluable view • Repetitive and consistent

Application Domain

Practical Lecture 6.1 – Remote Sensing Systems and Applications

• How to assess the damage? • Which are the most hit points?

• How to plan the Humanitarian aid?

A devastating tsunami hit many coastal regions in the Indian Ocean (2014)

• One of the most devastating natural disasters in recorded history • 14 countries were hit

[3] The 2004 Indian Ocean Tsunami [4] Five years after Indian Ocean tsunami

Application Examples (1)

Practical Lecture 6.1 – Remote Sensing Systems and Applications

 Global change detection and monitoring: Deforestation

 Environmental assessment and monitoring: Urban growth

[6] Deforestation in Bolivia from 1986 to 2001

[5] Population growth from 1975 - 2010 of Manila

Application Examples (2)

1986 2001Practical Lecture 6.1 – Remote Sensing Systems and Applications

• Perhaps the most common form of image interpretation

• Applications: environmental management, agricultural planning, health studies, climate and biodiversity monitoring, and land change detection

Classification of Remote Sensing Images

Generation of thematic maps

Practical Lecture 6.1 – Remote Sensing Systems and Applications

 Different tasks of equal importance

 Successful classification results depend on all the steps

Pipeline for Classification

 New analysis challenges

 5Vs: Volume, Variety, Velocity, Veracity and Value

 Need of scalable methods and underlying infrastructures

DATA ACQUISITION PRE-PROCESSING

EXTRACT/SELECT FEATURES CLASSIFICATION

POST- PROCESSING

CLASSIFICATION SYSTEM

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Platforms and Sensors

 Active Sensor: own source of illumination

 Capture image in day and night

 Any weather or cloud conditions

 Passive Sensor: natural light available

 Great quality satellite imagery

 Multispectral and Hyperspectral technology

 Platform: selected according to the application

[7] Active-and-passive-remote-sensing

DATA ACQUISITION PRE-PROCESSING

EXTRACT/SELECT FEATURES CLASSIFICATION

POST- PROCESSING

Practical Lecture 6.1 – Remote Sensing Systems and Applications

 Data are complex, noisy and may contain errors

 Improve image quality as the basis for later analyses that will extract information

 Detection and restoration of bad lines

 Geometric rectification

 Image registration

 Radiometric calibration

 Atmospheric correction

Preprocessing

DATA ACQUISITION PRE-PROCESSING

EXTRACT/SELECT FEATURES CLASSIFICATION

POST- PROCESSING

CLASSIFICATION SYSTEM Practical Lecture 6.1 – Remote Sensing Systems and Applications

Radiometric Calibration and Correction Process

[8] Radiometric Corrections

 The value recorded for a given pixel includes:

 reflected or emitted radiation from the surface

 radiation scattered and emitted by the atmosphere

 Most of the applications are interested in the actual surface values

Practical Lecture 6.1 – Remote Sensing Systems and Applications

The Feature Domain

 Select suitable features for successfully implementing an image classification

 How to obtain discriminating and independent features?

[9] G. Hughes

Too many features may decrease the classification accuracy

(1) : few raw features

(2): hundreds of raw features

DATA ACQUISITION PRE-PROCESSING

EXTRACT/SELECT FEATURES CLASSIFICATION

POST- PROCESSING

CLASSIFICATION SYSTEM

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Normalized Difference Vegetation Index (NDVI)

 Create additional relevant features from the existing raw features in the data

 Increase the predictive power of the classifier

NDVI = (NIR – R) / (NIR + R)

[11] NDVI

R G

B NIR

Bellingham, WA , US

[10] NDVI & ClassificationPractical Lecture 6.1 – Remote Sensing Systems and Applications

Spatial Information (1)  The scene complexity and the spatial resolution determines the number of mixed pixels

 The spectral unmixing problem:

 Identify the pure materials (endmembers)

 Estimate their corresponding proportions (abundances)

mixed spectral signature mixed pixel

65% 20% 5% 6% 4%

soft classification

 Two models to analyze the mixed pixel

[12] A. Plaza et al.

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Spatial Information (2)

 When spatial resolution increases, structures are larger than the pixel size

 The correlation between neighboring pixels increases

 Adjacent pixels of a roof pixel belong to the same class with a high probability

 Structures can be represented as regions of spatially connected pixels

The presence of mixed pixels can not be avoided

 Spatial contextual classifiers can exploit the correlation of pixels within a subset domain

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Classification

DATA ACQUISITION PRE-PROCESSING

EXTRACT/SELECT FEATURES CLASSIFICATION

POST- PROCESSING

CLASSIFICATION SYSTEM

 Model choice

 Mapping between low level features and high level information

 Training

 Training set must be representative

 Evaluation

 Measure the error rate (or performances)

 Computational Complexity

 Scalability

Practical Lecture 6.1 – Remote Sensing Systems and Applications

 World's largest single earth observation programme

 Directed by the European Commission in partnership with the European Space Agency (ESA)

 Monitors the Earth, its environment and ecosystems

 Free and open data policy

 Features: Continuity, Global coverage, Frequent updates and Huge data volumes

 Served by a set of dedicated satellites (the Sentinel families)

[13] Copernicus programme

[14] Sentinel Space

Copernicus

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Copernicus

[15] Sentinels for Copernicus Practical Lecture 6.1 – Remote Sensing Systems and Applications

Sentinel 2 Mission

• Platform: Twin polar-orbiting satellites, phased at 180° to each other

• Temporal resolution of 5 days at the equator in cloud-free conditions

 Provides