Improving Classification Accuracy Using Knowledge Based Approach Ali A. Alesheikh A. Talebzadeh F. Sadeghi Naeeni.

Improving Classification Accuracy Using Knowledge

Based Approach

Ali A. Alesheikh

A. Talebzadeh

F. Sadeghi Naeeni

Image interpretation by computer vision

Traditional strategiesTraditional strategies Knowledge-basedKnowledge-based

Levels of processing and representation Theory and concepts of knowledge-based system

Various errors in remotely sensed image analysis Various errors in remotely sensed image analysis Techniques for knowledge representation Techniques for knowledge representation

use of external knowledge for image interpretation Use of prior probabilities in the decision rule Use of prior probabilities in the decision rule Use of other images as external knowledgeUse of other images as external knowledge

Implementation

Image information

Image Analysis

Computer Graphics

ArtificialIntelligence

ImageProcessing

- Traditional strategies

use very little knowledge about the domainuse very little knowledge about the domain

the most commonly used the most commonly used approachesapproaches in RS in RS

have various problemshave various problems

- Knowledge-based image interpretation

tends to use more external information in the inference processtends to use more external information in the inference process

use spectral information in the use spectral information in the imageimage

GIS Knowledge base

Matching goal achievement

inference

Symbolic description

Hypothesis database

SegmentationSegmentationFeature Feature

extractionextractionPre-processingPre-processing

Image data

Levels of Levels of representrepresent

ationation

highhigh

IntermediateIntermediate(STM)(STM)

Levels of Levels of processingprocessing

lowlow

High (LTM)High (LTM)

lowlow

-Various errors in remotely sensed image analysis During data acquisition process


Nature of data

Adjacent pixels have influence on each otherAdjacent pixels have influence on each other


Nature of data


Land cover types do not fit into multiples of rectangular spatial unitsLand cover types do not fit into multiples of rectangular spatial units


Nature of data



Different surface materials may be distinguished by very Different surface materials may be distinguished by very subtle differences in their spectral patternssubtle differences in their spectral patterns

-Various errors in remotely sensed image analysis

During data acquisition process

Nature of data

During classification process



Different surface materials may be distinguished by very Different surface materials may be distinguished by very subtle differences in their spectral patternssubtle differences in their spectral patterns

Types of knowledge

Knowledge-a priori domain dependent

Knowledge-a priori domain dependent

declarativedeclarative

heuristicheuristic

algorithmalgorithm

inheritableinheritable

non-inheritablenon-inheritable

optionaloptional

essentialessential

negativenegative

relationalrelational

proceduralprocedural

objectobject

• semantic network

IF <condition> THEN <action>

represents objects and relations between objects as a graph structure i.e. a set of nodes connected by labeled arcs

In a frame-based system the objects at each node in the network is defined by a collection of attributed, slots, and values of thoseattributes, called fillers. Each slot can have procedures attached to it

• production rules

• frames or schemas

Rule #1

IF a pixel feature is (92,99,91) THEN it is “W (Wheat)” or “BID (Barely)” or “SB (Sugar beet)” or “ALO (Alfalfa)”.

Rule #2 IF a region in Aster's NDVI map is lower than 0.15 e

THEN it's crop type will be W (Wheat) or BID (barely).Rule #3

IF last year's crop was MS THEN in the interest year the crop will be W (Wheat).

Example of each knowledge representation techniques

BID W

Last year's crop was MS

ALO SB MS

MF

MG Maximum probability in traditional classification (e.g. maximum likelihood

classification)

Value on Aster's NDVI map on August

<0.15

is a

is a is a is a is a is a is a


Frame “W,BID,SB,ALO” slot: they are: W(Wheat),BID(Barely),SB(Sugar beet),ALO(Alfalfa). procedure: if identification of them is desired then search pixels that have maximum probability in any traditional classification like maximum likelihood classification.End frameFrame “W,BID” slots : they are: W(Wheat), BID(Barely). criterion for reconnaissance: they are harvested on the middle of June. procedures: if recognition of W or BID between recognized W, BID, SB, ALO is desired then search areas on Aster's NDVI map which is lower than 0.15.End frameFrame “W” slots : is: W(Wheat), is generalization of: W17, W22, WAT, WTN, WP, WKU, WGP. criterion for reconnaissance: for using the soil in the best way to producing crops, crop calendar disciplines must be considered. procedures : if reconnaissance of W between recognized W, BID is desired then we can use crop calendar disciplines, e.g. search the areas that their last year's crop was MS(Maize Seed). End frame


1

Realthreshold

Estimated threshold

Real distribution of

class 1

A posteriori probability of class 2 given equal a prior

probability

Probability

Feature

A posteriori probability of class 1 given equal A prior

probability Real distribution of

class 2

- Using of prior probability in the decision rule (maximum likelihood approach)

1

P{w k ,Xi }

P{X}P {w k| Xi} =

P{wk ,Xi } = (Xi) P{wk }

-p/2|e-1/2(X-'^ (-1)X-)

P{w k | Xi , v j} = Xi P{w k , v j }

K

k=1Xi P{w k , v j }

1

- Using of prior probability in the decision rule (maximum likelihood approach)

1

P{w k ,Xi }

P{X}P {w k| Xi} =

P{wk ,Xi } = (Xi) P{wk }

-p/2|e-1/2(X-'^ (-1)X-)

P{w k | Xi , v j} = Xi P{w k , v j }

K

k=1Xi P{w k , v j }

- Using of other images as external knowledge

The other knowledge for interpretation can be the other image which is acquired in the other time or with the other sensor. The resolution and spectral bands of the other image can be different from initial one.

Study area

• Moghan plain located in Ardebil

Study area


• About 300,000 tons of various crops produce annually in 18000 ha of irrigated farms.

Study area


• About 300,000 tons of various crops produce annually in 18000 ha of irrigated farms.

Corp Acreage(ha) Yield

Wheat 7000 up to 6500 kg/ha

Barely 1500-2000 up to 5000 kg/ha

Sugar Beet 3000 more than 50tons/ha

Maize Seed 15000 more than 2500 kg/ha

Maize Grain 1500 more than 6500kg/ha

Maize Silage 800 more than 40tons/ha

Alfalfa 1500 about 12tons/ha

Forage crops 700 20-100tons/ha

Available DATA:

• Maps of study area in 1/50000 scale

(UTM coordinate system and in WGS84 ellipsoid)

Available DATA:


(UTM coordinate system and in WGS84 ellipsoid) • Map of field boundaries

( production of polygonized fields)

Available DATA:




• Data about crop type of each field

Available DATA:





• ETM+ image (color composite 354)

(was acquired on 2001-05-23)

GIS of GIS of Moghan Moghan

FieldsFields

Available DATA:






(was acquired on2001-05-23)

• Aster image

(was acquired on August 2001-8-23)


FieldsFields

Available DATA:


(UTM coordinate system and in WGS84 ellipsoid)

Georeferenced

by map on 1/50000 scale

• Map of field boundaries




(was acquired on 2001-05-23)

• Aster image

(was acquired on August 2001-08-23)


FieldsFields

Experimental work

• Spectral-based :

Crop rotation patterns Times of planting and harvesting Field boundaries information Climate information

× Geographical information

Experimental work


• Knowledge-based : Crop rotation patterns Times of planting and harvesting Field boundaries information Climate information


Experimental work




Experimental work




× Financial information × Crop 'portfolio management' × Agricultural information × Advice centers

Spectral-based : rule matrices of every seven crop based on maximum likelihood

approach and equal prior probability





Overall accuracy of spectral-based classification = 53.2%.



Overall accuracy of spectral-based classification = 53.2%.

- Using of Crop Rotation Patterns :

TRANSITION MATRIX "1997-1998, 1998-1999"

ALO BID MF MG MS SB W

ALO 0.91 0 0 0 0 0 0.09

BID 0 0.38 0.22 0.14 0.09 0.17 0

MF 0 0.47 0 0 0 0.05 0.48

MG 0 0.09 0 0 0 0 0.91

MS 0 0 0 0 0 0 1

SB 0 0 0 0 0 0 1

W 0 0.17 0 0.07 0.32 0.38 0.06

• Transition matrix production

Knowledge-based classification :



ALO 0.86 0 0 0 0 0 0.14

BID 0 0.33 0.21 0.2 0.07 0.19 0

MF 0 0.49 0 0 0.04 0 0.47

MG 0 0.18 0.01 0 0 0 0.81

MS 0 0 0 0 0 0 1

SB 0 0.02 0 0 0.03 0 0.95

W 0 0.17 0.05 0.03 0.43 0.23 0.09

- Using of Crop Rotation Patterns :



ALO 0.91 0 0 0 0 0 0.09

BID 0 0.38 0.22 0.14 0.09 0.17 0

MF 0 0.47 0 0 0 0.05 0.48

MG 0 0.09 0 0 0 0 0.91

MS 0 0 0 0 0 0 1

SB 0 0 0 0 0 0 1

W 0 0.17 0 0.07 0.32 0.38 0.06

• Comparison between them Stable Dynamic System




ALO 0.86 0 0 0 0 0 0.14

BID 0 0.33 0.21 0.2 0.07 0.19 0

MF 0 0.49 0 0 0.04 0 0.47

MG 0 0.18 0.01 0 0 0 0.81

MS 0 0 0 0 0 0 1

SB 0 0.02 0 0 0.03 0 0.95

W 0 0.17 0.05 0.03 0.43 0.23 0.09

GIS Information extraction

Terrain object data (t-1)

Remote sensing data (t)

Application context

Updating

IF last year's crop = WheatTHEN current crop = Barely (17%), Maize feed (5%), Maize grain (3%),

Maize seed (43%), Sugar beet (23%), Wheat (9%).

Overall accuracy of maximum likelihood and estimated prior probability 66.7%.


- Times of planting and harvesting

Wheat and Barely are harvested on the June

Using of NDVI produced from Aster image which was acquiredon 23 August 2001

> 0.15< 0.15


- Times of planting and harvesting

Wheat and Barely are harvested on the June

Using of NDVI produced from Aster image which was acquiredon 23 August 2001

IF value of NDVI map is smaller than 0.15THEN crop type will be W(Wheat) or BID(barely)IF produced probability of W from the previous step is greater than probability of BIDTHEN crop type will be W(Wheat)

Overall accuracy of knowledge-based classification = 72.3 %.


- -Field boundaries Field boundaries informationinformation

In each field one crop typeIn each field one crop type

Overall accuracy of knowledge-based classification = 88.7 %.

conclusion

This paper shows us that "traditional image analysis seems to be like a random walk in problem space" and by using any external knowledge, known way can be selected for receiving the goal.

Future works• Crop rotation was used in this thesis. Transition matrices were produced from two

successive years. They can be extracted from three, four or more successive years.

• Other data sources can be used as external knowledge, e.g. the other bands of aster image can help us for interpretation.

• Knowledge about local soil types and conditions could be used to help predict likely crops to be planted.

• We can use geographical information as an external knowledge. E.g. economical constraints affect likelihood of crops. For example, crops with a high transportation cost and low profit margin may become less probable the further away from a storage silo the field is.

• Financial information can help us for image interpretation. By this fact that, farmers also base their decisions about which crops to plant based on market potentials, aiming to maximize profitability. Information about expected crop prices and likely future demand could again assist in classification

Improving Classification Accuracy Using Knowledge Based Approach Ali A. Alesheikh A. Talebzadeh F. Sadeghi Naeeni.

Documents

image slide

types of knowledge knowledge

knowledgebased classification

external knowledge use

concepts of knowledge

little knowledge

based image interpretation

traditional image analysis