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

Dr. Gabriele CavallaroPostdoctoral Researcher High Productivity Data Processing GroupJuelich Supercomputing Centre, Germany

Remote Sensing Systems and Applications (1)

October 16th, 2018Room V02 – 138

Cloud Computing & Big DataPARALLEL & SCALABLE MACHINE LEARNING & DEEP LEARNING

PRACTICAL LECTURE 6.1

Practical Lecture 6.1 – Remote Sensing Systems and Applications

Outline


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


Outline

Remote Sensing Background

Data Acquisition

Preprocessing

Feature Extraction and Selection

Copernicus: Sentinel 2 Mission


The term remote sensing was first used in the United States in the1950s by Ms. Evelyn Pruitt of the U.S. Office of Naval Research

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

• Measurement of radiation of differentwavelengths reflected or emitted fromdistant objects or materials

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

[1] Satellite (1960)

Remote Sensing

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

• Suitable for many applications

• Non-invasive method

• Satellite platforms• Invaluable view• Repetitive and consistent

Application Domain


• 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)


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


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/SELECTFEATURES CLASSIFICATION

POST-PROCESSING

CLASSIFICATION SYSTEM


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



POST-PROCESSING


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



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


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



POST-PROCESSING



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

RG

BNIR

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.


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


Classification



POST-PROCESSING


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


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


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 images of agriculture, forests, land-use change and land-cover change

Mapping biophysical variables, e.g., leaf chlorophyll/water content and leaf area index

Monitoring of coastal and inland waters and helping with risk and disaster mapping

[16] Earth Observation Mission Sentinel 2

~23 TB data stored per day


Copernicus Open Access Hub

[17] Copernicus Open Access Hub

Provides complete, free and open access to Sentinel-1, Sentinel-2 and Sentinel-3 products


Example of Data Retrieval (1)

Select the region of interest, the date and the product


Example of Data Retrieval (2)

Two tiles with different levels (i.e., L1C and L2A) are found over the region of interest


Products Visualization

Level-1Top-of-atmosphere reflectances

Level-2ABottom-of-atmosphere reflectance


Sentinel 2 Products Type Sentinel-2 tile gridding is based on the NATO Military Grid Reference System

Each tile covers an area of 100 × 100 𝐾𝐾𝐾𝐾2 (excluding overlapping edges of 9.8 𝐾𝐾𝐾𝐾)

[18] Military Grid Reference System [19] Sentinel-2 product types

Germany can be covered with 56 tiles

E.g., Time serie of tile images for 1 year(365 days / 5 days = 73 acquisitions)

Tile

…

Data size to be processed: 73 acq. * 56 tiles * 800Mb = 3.11 TB


Updating land-cover maps is an important task for regularly monitoring the Earth’s surface

Generation of reliable maps with spatial consistency is challenging Use of data acquired from several satellite orbit tracks observed at different dates Presence of clouds

Map production cannot rely on field campaigns (huge amounts of data would need to becollected)

Existing databases are used to build the reference data sets needed for the supervisedclassification

[21] France land cover classification 2016 [22] S2 prototype LC map at 20m of Africa 2016

[20] CORINE land cover

Sentinel 2 - Update of Land-Cover Maps at Country Scale


Sentinel 2 – Land Cover Map of France 2017

[23] CESBIO: 2017 Land Cover Map


Lecture Bibliography (1) [1] CORONA: American’s First Satellite Program: first photograph.

Online: https://www.oneonta.edu/faculty/baumanpr/geosat2/RS%20History%20II/RS-History-Part-2.html [2] The Earth-Atmosphere Energy Balance

Online: http://theatmosphere.pbworks.com/w/page/27058542/The%20Earth-Atmosphere%20Energy%20Balance [3] The 2004 Indian Ocean Tsunami

Online: https://www.thoughtco.com/the-2004-indian-ocean-tsunami-195145 [4] Five years after Indian Ocean tsunami, affected nations rebuilding better – UN

Online: http://www.un.org/apps/news/story.asp?NewsID=33365#.WltdyK6nGpp [5] Population growth from 1975 - 2010 of Manila

Online: https://manilabydaniellaandisabel.weebly.com/location-and-characteristics.html [6] Deforestation in Bolivia from 1986 to 2001

Online: https://www.satimagingcorp.com/gallery/more-imagery/aster/aster-deforestation-bolivia/ [7] Active-and-passive-remote-sensing

Online: http://grindgis.com/remote-sensing/active-and-passive-remote-sensing [8] Introduction to Remote Sensing: Radiometric Corrections

Online: http://gsp.humboldt.edu/olm_2015/Courses/GSP_216_Online/lesson4-1/radiometric.html [9] G. Hughes, "On the mean accuracy of statistical pattern recognizers," in IEEE Transactions on Information Theory, vol.

14, no. 1, pp. 55-63, 1968 [10] NDVI & Classification

Online: https://lholmesmaps.wordpress.com/my-work-2/environmental-studies-421-gis-iv-advanced-gis-applications/2-2/


https://www.oneonta.edu/faculty/baumanpr/geosat2/RS%20History%20II/RS-History-Part-2.htmlhttp://theatmosphere.pbworks.com/w/page/27058542/The%20Earth-Atmosphere%20Energy%20Balancehttps://www.thoughtco.com/the-2004-indian-ocean-tsunami-195145http://www.un.org/apps/news/story.asp?NewsID=33365#.WltdyK6nGpphttps://manilabydaniellaandisabel.weebly.com/location-and-characteristics.htmlhttps://www.satimagingcorp.com/gallery/more-imagery/aster/aster-deforestation-bolivia/http://grindgis.com/remote-sensing/active-and-passive-remote-sensinghttp://gsp.humboldt.edu/olm_2015/Courses/GSP_216_Online/lesson4-1/radiometric.htmlhttps://lholmesmaps.wordpress.com/my-work-2/environmental-studies-421-gis-iv-advanced-gis-applications/2-2/

Lecture Bibliography (2) [11] Normalized Difference Vegetation Index (NDVI)

Online: http://www.agasyst.com/portals/NDVI.html [12] A. Plaza, G. Martín, J. Plaza, M. Zortea and S. Sánchez, “Recent Developments in Endmember Extraction and Spectral

Unmixing“ , in Optical Remote Sensing, vol 3. Springer, Berlin, Heidelberg, 2011 [13] Copernicus programme: Europe’s eyes on Earth

Online: http://www.copernicus.eu/ [14] Sentinel Space

Online: http://earsc.org/news/airbus-selected-by-esa-for-copernicus-data-and-information-access-service-dias [15] Sentinels for Copernicus

Online: https://www.youtube.com/watch?v=xcflQZJ5n88 [16] Earth Observation Mission Sentinel 2

Online: https://sentinel.esa.int/web/sentinel/missions/sentinel-2 [17] Copernicus Open Access Hub

Online: https://scihub.copernicus.eu/dhus/#/home [18] Military Grid Reference System

Online: https://en.wikipedia.org/wiki/Military_Grid_Reference_System [19] Sentinel-2 product types

Online: https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types [20] CORINE land cover

Online: https://land.copernicus.eu/pan-european/corine-land-cover


http://www.agasyst.com/portals/NDVI.htmlhttp://www.copernicus.eu/http://earsc.org/news/airbus-selected-by-esa-for-copernicus-data-and-information-access-service-diashttps://www.youtube.com/watch?v=xcflQZJ5n88https://sentinel.esa.int/web/sentinel/missions/sentinel-2https://scihub.copernicus.eu/dhus/#/homehttps://en.wikipedia.org/wiki/Military_Grid_Reference_Systemhttps://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-typeshttps://land.copernicus.eu/pan-european/corine-land-cover

Lecture Bibliography (3) [21] France land cover classification 2016

Online: http://www.cesbio.ups-tlse.fr/multitemp/?p=11778 [22] S2 prototype LC map at 20m of Africa 2016

Online: http://2016africalandcover20m.esrin.esa.int/ [23] CESBIO: 2017 Land Cover Map

Online: http://osr-cesbio.ups-tlse.fr/~oso/


http://www.cesbio.ups-tlse.fr/multitemp/?p=11778http://2016africalandcover20m.esrin.esa.int/http://osr-cesbio.ups-tlse.fr/%7Eoso/

Dr. Gabriele CavallaroPostdoctoral Researcher High Productivity Data Processing GroupJuelich Supercomputing Centre, Germany

Remote Sensing Systems and Applications (2)October 16th, 2018Room V02 – 138

Cloud Computing & Big DataPARALLEL & SCALABLE MACHINE LEARNING & DEEP LEARNING

PRACTICAL LECTURE 6.1


Outline


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 3 / 56

Outline

4 / 60

Machine Learning Background

Deep Learning and Shallow Learning

Indian Pines Hyperspectral Dataset

Hyperspectral Image Classification

Challenges of Remote Sensing with Deep Learning


Classical Pattern Recognition System

Patter recognition is the science of making inferences from perceptual data, using toolsfrom statistics, probability, computational geometry, machine learning, signal processingand algorithm design

• Machine learning: term introduced by Arthur Samuel in 1959:• Gives computers the ability to learn without being explicitly programmed • Explores the study and construction of algorithms that can learn from and

make predictions on data

• Today: Machine Learning is a huge (growing) field



POST-PROCESSING



• Unsupervised learning:

• No labels are given to the learning algorithm

• Goal: find structure in its inputs

• Used to discover hidden patterns in data, learn features

• Clustering

Machine Learning Methods

[1] K-means in Python 3 on Sentinel 2 dataPractical Lecture 6.1 – Remote Sensing Systems and Applications

Supervised learning:

The computer is presented with examples inputs and desired outputs= training data D = {(𝒙𝒙,𝑦𝑦)}

The goal is to learn a general rule 𝑓𝑓𝑤𝑤 that maps inputs to outputs to outputs 𝑓𝑓𝑤𝑤 𝒙𝒙 = 𝑦𝑦


?

Classification: 𝑦𝑦 is a nominal number (i.e., a class label)

Regression: 𝑦𝑦 is a continuous number


Domain adaptation:

Aims at learning from a source data distribution a well performing model on a different (but related) target data distribution

E.g., Acquisitions on different dates, with different sensors, etc.


Example of a shift in the signature ofa hyperspectral image acquired bythe Hyperion sensor over two areas ofthe Okavango Delta in Botswana

[2] D. Tuia, C. Persello and L. Bruzzone


Deep Learning and Shallow Learning

Shallow learning: learning networks that usually have at most one to two layers

They compute linear or nonlinear functions of the data (often hand-designed features)



POST-PROCESSING

DL means a deeper network with many layers of non-linear transformations

No universally accepted definition of how many layers constitute a “deep” learner

Typical networks are typically at least four or five layers deep


http://www.cs.utoronto.ca/~rgrosse/cacm2011-cdbn.pdf


Deep Networks Learn Hierarchical Feature Representations

[3] H. Lee et al.

• Classification: make a prediction for a whole input

• What are the classes and ranked list

• Localization or detection: towards fine-grained inference

• Classification and spatial location (e.g., bounding boxes)

• Semantic segmentation: fine-grained inference

• Make dense predictions inferring labels for every pixel

• Further improvements: provide different instances of the same class

• Decomposition of already segmented classes into their components

• Many applications nourish from inferring knowledge from imagery

• Autonomous driving

• Human-machine interaction

• Computational photography

• Image search engines

• Remote sensing Practical Lecture 6.1 – Remote Sensing Systems and Applications

Progression from Coarse to Fine Inference

[4] A. Garcia-Garcia

[5] Image Segmentation

Classification of Hyperspectral Images

1DSPECTRAL

2DSPATIAL

3DSPECTRAL +SPATIAL

3D Convolution Consider data as a volume Local features (spatial and spectral) Input/output: 3D tensor


Break the images into many small crops and classify the central pixel

Redundant and computationally expensive

Stores not only every pixel but also the surrounding pixels

Increases the data size by a factor determined by the number of neighbouring pixels

Advantage of using spectral and spatial information in the classification process

More visible with Hyperspectral images

Building

Building

Tree

Extract patchClassify center pixel with CNN


Classification Approach: Sliding Window

Example of Hyperspectral Dataset – Indian Pines

Acquired by the Nasa’s Airborne Visible/Infrared ImagingSpectrometer (AVIRIS) on June 12, 1992

Covers a mostly agricultural and wooded area in the westof the Purdue University in Indiana (USA)

The image consists of 1417 × 617 pixels with a spatialresolution of around 20 m

For each pixel the image provides 220 spectral channelscovering wavelengths in the range of 0.4 𝜇𝜇m to 2.5 𝜇𝜇m(i.e., ‘cube’)

The pixel-wise labelling was done by M. Baumgardner andher students

This resulted in a label-map of the dataset

[6] P. U. R. Repository [7] M.F. Baumgardner et al.


Hyperspectral Image Classification – Indian Pines

Practical Lecture 6.1 – Remote Sensing Systems and Applications [8] G. Cavallaro, et al.

58 different classes Distribution of the number of samples per class is highly unbalanced

Biggest class contains >60.000 pixels whereas there are several classes with

Class score

Series of convolution and pooling layers

Fully connected layers

Convolutional layers: convolution operation on the input

Emulate the response of an individual neuron to visual stimuli

Each convolutional neuron processes data only for its receptive field

Polling layers: progressively reduce the spatial size of the representation

Reduce the amount of parameters and computation and control overfitting

Fully connected layers connect every neuron in one layer to every neuron in another layer

Same principle as the traditional multi-layer perceptron (MLP) network

Image Classification CNNs

[9] J. Long et al.


CNN Classifier for Hyperspetral Image - Architecture


Classify pixels in a hyperspectral remote sensing image having groundtruth/labels available

Created CNN architecture for a specific hyperspectral land cover type classification problem

Performed no manual feature engineering to obtain good results (aka accuracy)

Keras – Remote Sensing CNN ‘Standard‘ Model


Experimental Setup – Results – Full Dataset – Accuracy


SVM comparison~ 77% withmanual featureengineering

Experimental Setup – Results – Full Dataset – Class Checks


Experimental Setup – Results – Full Dataset – Class Checks

Blue: correctly classified / training data Red: incorecctly classified


RS applications have massive amounts of temporal and spatial data (e.g., Sentinel 2)

But not enough labeled training samples, which usually don’t fully represent: Seasonal variations Object variation (e.g., plants, crops, etc.)

Most online hyperspectral data sets have little-to-no variety

DL systems with many parameters require large amounts of training data Else they can easily overtrain and not generalize well

DL systems in CV use very large training setse.g., millions or billions of faces in different illuminations, poses, inner class variations, etc.


Limited Remote Sensing Training Data

[6] P. U. R. Repository

Possible approaches to mitigate small training samples:

1. Data augmentation Affine transformations, rotations, small patch removal, etc.

2. Transfer learning Train on other imagery to obtain low-level to mid-level features

3. Use ancillary data Other sensor modalities (e.g., LiDAR, SAR, etc.)

4. Unsupervised training training labels not required


DL systems with limited training data

State of the art DL networks have parameters in the order of millions

The learning model needs a proportional amount of examples

The number of parameters should be proportional to the complexity of the task


Need for a Large Amount of Training Data

[10] Data Augmentation

The available dataset is taken in a limited set of conditions

Different orientation, location, scale, brightness etc.

“A poorly trained neural network would think that these three tennis balls, are distinct, unique images”

Train with additional synthetically modified data

Techniques to artificially increase the size of the training set

Make minor changes such as flips, translations and rotations to the existing dataset

Employed to counteract overfitting


Data Augmentation


Ability to recognize an object as an object, even when its appearance varies in some way

It allows to abstract an object's identity from the specifics of the visual input

E.g., relative positions of the viewer/camera and the object.

Well-trained CNNs can be invariant to translation, viewpoint, size or illumination


Essential Assumption: Invariance

[11] Invariance property

Flip horizontally and vertically

Rotate

Scaled outward or inward

Crop: random sample a section

Translate: moving the image along the X or Y direction

Add noise

Data augmentation is more challenging for remote sensing

Images exist in a variety of conditions (e.g., different seasons)

They cannot be accounted for by the above simple methods



Popular Augmentation Techniques


Master's Theses

I provide supervision for Master's thesis topics that included methods for processing and analyzing remote sensing data (from very high spatial resolution to hyperspectral)

Classic machine learning approaches and more advanced deep learning algorithms

Priority to the scalability (use of HPC systems)

Feel free to contact me ([email protected]) to discuss further

mailto:[email protected]

Lecture Bibliography (1) [1] K-means in Python 3 on Sentinel 2 data

Online: http://www.acgeospatial.co.uk/k-means-sentinel-2-python/ [2] D. Tuia, C. Persello and L. Bruzzone, "Domain Adaptation for the Classification of Remote Sensing Data: An Overview of

Recent Advances," in IEEE Geoscience and Remote Sensing Magazine, vol. 4, no. 2, pp. 41-57, June 2016. doi:10.1109/MGRS.2016.2548504

[3] H. Lee, R. Grosse, R. Ranganath and A. Y. Ng, “Unsupervised Learning of Hierarchical Representations with ConvolutionalDeep Belief Networks”, in Commun. ACM, vol. 54, no. 10, pp. 95-103, 2011.

[4] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, J. Garcia-Rodriguez, “A Review on Deep LearningTechniques Applied to Semantic Segmentation”, in CoRR, 2017.Online: http://arxiv.org/abs/1704.06857

[5] Image Segmentation Using DIGITS 5Online: https://devblogs.nvidia.com/image-segmentation-using-digits-5/

[6] P. U. R. Repository. 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3, 2015, Online: https://purr.purdue.edu/publications/1947/about?v=1

[7] M. F. Baumgardner, L. L. Biehl, and D. A. Landgrebe. 220 band aviris hyperspectral, image data set: June 12, 1992 indian pine test site 3. Purdue University Research Repository, 2015.

[8] G. Cavallaro, M. Riedel, J.A. Benediktsson et al., ‘On Understanding Big Data Impacts in Remotely Sensed Image Classification using Support Vector Machine Methods’, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2015

[9] J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3431-3440.

[10] Data Augmentation - How to use Deep Learning when you have Limited DataOnline: https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-

c26971dc8ced [11] Invariance property

Online: https://i.stack.imgur.com/iY5n5.png


http://www.acgeospatial.co.uk/k-means-sentinel-2-python/http://arxiv.org/abs/1704.06857https://devblogs.nvidia.com/image-segmentation-using-digits-5/https://purr.purdue.edu/publications/1947/about?v=1https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8cedhttps://i.stack.imgur.com/iY5n5.png

1_Remote_Sensing_Systems_and_ApplicationsCloud Computing & Big DataPARALLEL & SCALABLE MACHINE LEARNING & DEEP LEARNINGOutlineOutline of the CourseOutlineRemote SensingSlide Number 6Application Examples (1)Application Examples (2)Classification of Remote Sensing Images Pipeline for ClassificationPlatforms and Sensors PreprocessingRadiometric Calibration and Correction ProcessThe Feature DomainNormalized Difference Vegetation Index (NDVI)Spatial Information (1)Spatial Information (2)ClassificationSlide Number 19Slide Number 20Sentinel 2 MissionCopernicus Open Access HubExample of Data Retrieval (1)Example of Data Retrieval (2)Products VisualizationSentinel 2 Products TypeSlide Number 27Slide Number 28Lecture Bibliography (1)Lecture Bibliography (2)Lecture Bibliography (3)

2_Remote_Sensing_Systems_and_ApplicationsCloud Computing & Big DataPARALLEL & SCALABLE MACHINE LEARNING & DEEP LEARNINGOutlineOutline of the CourseOutlineClassical Pattern Recognition SystemMachine Learning MethodsMachine Learning MethodsMachine Learning MethodsDeep Learning and Shallow LearningDeep Networks Learn Hierarchical Feature RepresentationsProgression from Coarse to Fine Inference Classification of Hyperspectral ImagesClassification Approach: Sliding WindowExample of Hyperspectral Dataset – Indian PinesHyperspectral Image Classification – Indian PinesImage Classification CNNsCNN Classifier for Hyperspetral Image - Architecture Keras – Remote Sensing CNN ‘Standard‘ ModelExperimental Setup – Results – Full Dataset – Accuracy Experimental Setup – Results – Full Dataset – Class ChecksExperimental Setup – Results – Full Dataset – Class ChecksSlide Number 22Slide Number 23Slide Number 24Data Augmentation Essential Assumption: InvarianceSlide Number 27Slide Number 28Lecture Bibliography (1)

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

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