remote sense big data analytics

7/23/2019 Remote Sense Big Data Analytics

http://slidepdf.com/reader/full/remote-sense-big-data-analytics 1/30

Mohamad Ivan Fanany, Sidik Mulyono, T. Basaruddin

Faculty of Computer Science Universitas Indonesia

in cooperation with Indonesian Agency of Assessment and Application of Technology (BPPT

Deep Learning for Big Data

in Remote Sensing



High Resolution Earth Observation

Increasing Complexity, Diversity, Dimensionality,



• Increasing complexity of RS data diversity and higher

dimensionality!

• "arge data#intensive issues tas$s and data

dependencies

• Current platform are C%&#heavy but I'O# poor (hich do

not (or$ non#contiguously!

Remote Sensing )ig Data

Challenges



Remote Sensing )ig Data

Opportunities from *ech! "eapfrogging

+! Some supercomputers and Cloud computing

platforms optimied for data#intensive loads-

.! %arallel file systems and databases ta$es

the data availability and locality as the mainconcern-

/! *he data managing tools for memory data

placement controlling for multilevel data

locality-

0! *as$ scheduling focusing on large amount of

dependent tas$s and data availability!



Remote Sensing Data %rocessing 1lo(



Remote Sensing Utilization for

Indonesian Food Se!rit" Program

Rie field area# $%1 million &etares

Pop!lation# 24' million

(ational rie need# 32 million )*"ear


http://slidepdf.com/reader/full/remote-sense-big-data-analytics 7/30Page +S,-PLI(. ,R/,# 0araang est aa Indonesia

'6 15 46%24S 1'+ 25 '5%1/7

8"perspetral 8"-ap Image

Padd" .rot& Stages 9lassifiation



Page $

)&e omposited Images from 1'

lines of 8"-ap 8"perspetral

,ir:orne 9ampaign

0araang 2'117

Padd" "ield distri:!tion map

it& .,;(SP9R)otal prod!tion# 1$6$<$ )on

8arested ,rea# 3'<66 8a =ield aerage# 6%'' )on*8a

Rie =ield /stimation



Page <

8"-ap 8"perspetral

4%4 m spatial res124 :and spetral res7

->DIS -!ltispetral

1%''' m spatial res

+ :and sptral res7

8"perspetral :ased

->DIS integration model

=ield

predition

.rot&

stages

Remote Sensing Data F!sion for

Integrated Predition



Page 1'

Statistial ?alidation

>erall a!ra"@ $4%<6A



RecentReent Res!lts after 9lo!d Detetion

and Remoal >nl" on ->DIS Data



RecentReent Res!lts after 9lo!d Remoal

>nl" on ->DIS Data



Saling Up -odel B!ilding

Be"ond S!persite Limited /stimation



Deep Learning for ,!rate Land

9oer and Land Use Predition



Page 1+





Page 1$

,nal"sis on DeepS,) Datasets

Bas! et al% 2'157

Hand Crafted 1eatures as Deep "earning Input,



Page 1<


Bas! et al% 2'157

Classification 2ccuracy using D)3,

Conventional D" %roposed D"



Page 2'


Bas! et al% 2'157

Classification 2ccuracy using C33,




Bas! et al% 2'157

Classification 2ccuracy using SD2E



&" )raditional Deep ,r&itet!res

are (ot /no!g& for S,) Datasets

• Satellite datasets have higher intra and inter#class

variability!

• *he amount of labeled data is much smaller as

compared to the total sie of the dataset!• Higher#order texture features are a very important

discriminative parameter for various land cover

classes!

• Shape'edge based features are not very useful inlearning data representations for satellite imagery!

• Spatially contextual information is another important

parameter fo!r modeling satellite imagery!



Satelite Image Feat!re RanCing



&at are e !rrentl" doing

Proposal :" )anaCa et al% 2'13



Page 2+





Page 2$





Page 2<

&at e are !rrentl" doing

Some Lingering !estionsE

• Ho( far Deep "earning can go4

– *exture based prediction of vegetation and its

gro(th stages

– 51latten6 fre7uency bands of 8ODIS or evenHyperspectral data into a single channel for

classification4

• 2re commonly used remote sensing parameters

such as 3D9I, E9I, :%% in multi or hyper bands

based classification have higher Distribution

Separation Criterion4



Page 3'

)8,(0 =>U; < 2

remote sense big data analytics

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