Fuzzy feature-based image mining in remote sensing

Fuzzy feature-based image mining in remote sensing Jiang Lia, Ram M. Narayanana, William J. Waltmanb, and Albert J. Petersc a

b

c

Environmental Remote Sensing Laboratory Center for Electro-Optics Department of Electrical Engineering University of Nebraska-Lincoln Lincoln, NE 68588-0511 USA

Department of Computer Science and Engineering University of Nebraska-Lincoln Lincoln, NE 68588-0115 USA

Center for Advanced Land Management Information Technologies Conservation and Survey Division Institute of Agriculture and Natural Resources University of Nebraska-Lincoln Lincoln, NE 68588-0517 USA ABSTRACT

The problem of image mining combines the areas of content-based image retrieval (CBIR), image understanding, data mining and databases. Image mining in remote sensing is more challenging due to its multi-spectral and spatio-temporal characteristics. To deal with the phenomena that have imprecise interpretation in remote sensing applications, images should be identified by the similarity of their attributes rather than exact matching. Fuzzy spatio-temporal objects are modeled by spatial feature values combined with geographic temporal metadata and climatic data. This paper focuses on the implementation of a remotely sensed image database with fuzzy characteristics, and its application to data mining. A comprehensive series of calibrated, geo-registered, daily observations, and biweekly maximum NDVI composite AVHRR images are processed and used to build the database. The particularity of the NDVI composite images that our experiments are conducted on is that they cover large geographic areas, and are suitable to observe seasonal changes in biomass ("greenness”). Based on the characterization of land cover and statistical analysis of climatic data related to NDVI, spatial and temporal data mining such as abnormality detection and similar time sequences detection were carried out by fuzzy object queries. Keywords: image retrieval, data mining, remote sensing, fuzzy spatio-temporal object, object-oriented database

1. INTRODUCTION It has been observed that “Mining useful content from image collections is very much an interdisciplinary endeavor that draws upon expertise in computer vision, image understanding, data mining, machine learning, databases, distributed/parallel computing, software design and artificial intelligence”.1 The problem of image mining may be seen as similar to image processing. However, in the case of image mining studies, very large among of data are processed, while image processing usually concentrates on analysis of single or few images. Generally, the tasks related to the interpretation of remote sensing data assume that the source of information is just one image. The methods applied for information extraction are image enhancement, image segmentation, fitting physical models to the data, etc. A more accurate interpretation of remotely sensed scenes is obtained by synergistically combining images from different sensors. One of the difficulties in many data fusion algorithms is the identification of data sets relevant for the user, where the information retrieval task becomes one of image mining.2 Remote sensing image mining aims to discover statistical rules and patterns automatically from a large volume of remotely sensed images. The measure of what is meant by “interesting to the user” is dependent on the user as well as the application. Dealing with different types of data and incorporating them within a single discovery process is often a very difficult problem.

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Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, Belur V. Dasarathy, Editor, Proceedings of SPIE Vol. 4384 (2001) © 2001 SPIE · 0277-786X/01/$15.00

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The state of the art remote sensing image archives are managed by relational database management systems able to query the data with no reference to the information content, except visual browsing using quick-look images. This technology is limited to applications involving a reduced number of images of images of medium resolution. An alternative approach is to develop a spatio-temporal objected-oriented database based on the features extracted from interesting regions of the remote sensing images and ancillary data sets.3 Meteorological satellite images such as Advanced Very High Resolution Radiometer (AVHRR) have been used by many researchers to map, monitor and evaluate vegetation. AVHRR data are collected daily over most of the earth's surface, which facilitates observations of vegetation dynamics. The 1.1-km spatial resolution of the data allows the study of very large areas with datasets of manageable size. Observed changes in the Normalized Difference Vegetation Index (NDVI) through time are generally thought to reflect vegetation type, phenology and local environmental conditions. The NDVI is usually assumed to be broadly indicative of, and associated with, plant photosynthetic activity and aboveground primary production.4 Some researchers have discovered the complex relationships between the NDVI and various ecoclimatological variables. The spatial and temporal patterns of NDVI have been linked with temperature and precipitation patterns5 and plant evapotranspiration.6 The NDVI has also been employed as an indicator of regional drought condition,7 and as an index of the interannual variability of certain climate variables.8 A study investigating relationships between the AVHRR NDVI and some climate variables and their variability over various time and space scales in the central Great Plains has been carried out.9 In this paper, we present the description of a model to detect abnormality and time sequential patterns of the crops and grassland “greenness”, using the results of the above research studies as the domain knowledge. To deal with the phenomena that have imprecise interpretation in such remote sensing applications, image objects are identified by the similarity of their attributes rather than exact matching.10 The fuzzy object-oriented database architecture used in this research is based on the fuzzy object-oriented data (FOOD) model presented in Refs. 11 & 12, and fuzzy content-based image retrieval prototype presented in Refs. 13 & 14.

2. DATA PREPARATION The process of data preparation is to create a target data set and to focus attention on a subset of variables or data samples on which mining is to be performed. Data preparation for mining on remote sensing images is even more challenging due to the diverse nature of different data sets with respect to spatial and temporal resolutions. There are three data sets used in this study: AVHRR NDVI data, daily climatic data, and statewide land cover/use map.

2.1. AVHRR NDVI Data Biweekly AVHRR maximum value composite (MVC) 1.1-km resolution NDVI images covering the state of Nebraska and encompassing the period from 1989 to 1998 mid-growing seasons (April-September) are used in this study. The MVC NDVI images were generated by the U.S. Geological Survey/EROS Data Center.15 The AVHRR images without major cloud cover were selected for compositing. Radiometric calibration of visible (VIS, channel 1) and near-infrared (NIR, channel 2) was first accomplished. Then solar illumination variability was corrected, and geometric registration was applied. The NDVI is defined as the difference of near-infrared and visible reflectance values normalized over the sum of channels 1 and 2, as shown below:

NDVI =

NIR-VIS

(1)

NIR + VIS

The NDVI equation produces values in the range of -1.0 to 1.0, where increasing positive values indicate increasing green vegetation and negative values indicate non-vegetated surface features such as water, barren, ice, snow, or clouds. To scale the computed NDVI results to byte data range, the NDVI computed value, which ranges from -1.0 to 1.0, is scaled to the range of 0 to 200, where computed -1.0 equals 0, computed 0 equals 100, and computed 1.0 equals 200. As a result, scaled NDVI values less than 100 represent clouds, snow, water, and other non-vegetative surfaces, while values equal to or greater than 100 represent vegetative surfaces.

2.2. Daily Climatic Data Surface meteorological data as shown in Table 1 were collected at twenty-five Automated Weather Data Network (AWDN) station data in Nebraska between 1989 and 1998, and processed by the High Plains Climate Center (HPCC) of the University at Nebraska-Lincoln (UNL). Three climate variables from this data set were used: soil temperature, precipitation, and

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potential evapotranspiration. Another set of climatic data used in this research is precipitation data from over two hundred National Weather Service (NWS) stations surrounding the AWDN stations, and covering nearly the whole of Nebraska. Table 1. Example of AWDN climatic data samples.

a250059 AINSWORTH NE Lat.(deg.min)= 42.55 Long.(deg.min)= 99.82 Elev.(m)= 765. Date

T-High o F

T-Low o F

Rel Hum %

… 7/7/1990 7/8/1990 7/9/1990 …

… 100.706 75.776 71.582 …

… 67.046 65.084 60.548 …

… 45.340 72.869 78.083 …

Soil Tmp F@4inch

WindSpd mi/hr

Solar langleys

Precip inches

ET-NE inches

… 83.506 74.920 70.349 …

… 10.862 6.112 5.702 …

… 642.533 341.164 214.575 …

… 0.118 0.039 0.039 …

… 0.464 0.164 0.106 …

o

2.3. Statewide Land Cover/Use Map Land cover/use map were derived from the conterminous U.S. 1990 Land Cover Characteristics Database jointly developed by EROS Data Center and the UNL Center for Advanced Land Management Information Technologies (CALMIT).16 Eight land cover types were identified to determine the surface cover conditions in the vicinity of the weather stations, and these are shown in Figure 1.*

Figure 1. Eight land cover/use types in Nebraska.

3. DATA CLEANING AND PREPROCESSING Data cleaning and preprocessing is the most time-consuming step in Knowledge Discovery in Database (KDD) process. This deals with collecting the necessary information to model or account for noise in the data, deciding on strategies for handling missing data fields, and accounting for time sequence information and known changes.17

3.1. Scene Selection and Data Cleaning The MVC NDVI data used in this study are cloud-free by biweekly composition. Because the vegetation index has no meaning over water, MVC NDVI data were recomposed by a water mask map to monthly water masked NDVI data (MWM NDVI). As the MWM NDVI image contains no clouds, water, and we assume there is no snow during the mid-growing season from April to September, all the pixel value ranges from 100 to 200. Then the MWM NDVI data were used to build the image database, and to produce the Standardized Vegetation Index (SVI) maps. There are lots of missing data in the climatic data sets. A flagging system for HPPC AWDN stations and NWS stations was used to label these missing data into four classes: e.g. M ⇒ the data is missing; E ⇒ we feel we have a reliable estimate; R ⇒ estimate based on weighted linear regression from surrounding stations; e ⇒ we are not confident of the estimate. A statistical analysis was applied to the climatic data to calculate the monthly highest, lowest, and average soil temperature, total precipitation, and total evapotranspiration. *

The color image for Figure 1 can be downloaded from http://doppler.unl.edu/~jerry/Jiang_Research.html.

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3.2. SVI Calculation SVI is calculated by computing the long-term mean and standard deviation for each month from 1989 through 1998 at each 1.1-km pixel location. These statistics are used to standardize the vegetation index value through the use of the statistical Zscore, which is a random variable with a normal probability distribution function. The probability of Z-score represents the degree of current vegetation status relative to normal conditions. Five classes were created as shown in Table 2. SVI is used to classify the status of “greenness” in this study. Table 2. Classification of the relative vegetation status.

Probability of Z-score

Vegetation status

0 – 0.05

Very poor

0.05 – 0.25

Poor

0.25 – 0.75

Normal

0.75 – 0.95

Good

0.95 – 1

Very good

3.3. Image Segmentation Each MWM NDVI image was first masked to obtain a subset image covering Nebraska only with resolution 750 x 350 pixels. Several segmentation methods can then be applied to decompose each image. There are many techniques for decomposition such as the Quad-Tree segmentation,18 and decomposition using evolutionary strategies.19 In this study, we use a straightforward application-oriented decomposition method based on the available domain knowledge, which is to decompose the images using the land cover/use types and the location of the weather stations. After labeling the areas of interest (AOI) for each land cover/use type in one image for a specific month, e.g. April 1989, the time series image tiles covering the growing season for each year were built for these areas. This permits the user query for a certain land cover/use type. Alternatively, a square image tile is created of side 64 km (64 x 64 pixels) by locating a weather station at its center. A multi-layer image covering the mid-growing season is then decomposed by the location (e.g. the longitude and the latitude coordinate) of the weather stations. Some researchers develop labels using circles. However, we use a square instead of a circle to facilitate the image representation. A multi-layer image decomposition example is shown in Figure 2, where N could be the index of the AOI of a given land cover/use type or the serial number of a given weather station.

Figure 2. Multi-layer image decomposition.

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4. SYSTEM ARCHITECTURE To study relationship between the vegetation condition and the climatic parameters, image attributes and the statistical climatic parameters related to the status of the NDVI greenness are stored in the Fuzzy Object-Oriented Database (FOODB). The raw images are stored in a separate image database, and only the pointers to the corresponding images are maintained in the FOODB. The fuzzy queries are applied to the FOODB, but it is also easy to retrieve the images in the image database. The overall architecture of the system is shown as Figure 3. The FOODB used in this study are based on the FOOD model and the fuzzy content-based retrieval prototype presented in Refs. 11-14, with extensions to fuse the climatic parameters, and especially to fit the specific time series image mining task.

Figure 3. System architecture.

4.1. Fuzzy Object Representation The FOOD is discussed in detail in Ref. 11, and is briefly described here. A fuzzy domain is defined for each fuzzy attribute, and a similarity matrix is used to represent the relation within the each of the fuzzy attributes. “The domain, dom , is the set of values the attribute may take, irrespective of the class it falls into. The range of an attribute, rng , is the set of allowed values that a member of a class, i.e. an object, may take for an attribute. In general rng ⊆ dom .”11 A range for each attribute of the class is a subset of the corresponding fuzzy domain. The range definition for attribute ai of class C is donated as rng c ( ai ) , where ai ∈ Attr (C ) = { a1 , a2 ,..., an } , and Attr (C ) refers to the attributes of class C .11 Similar objects are grouped together to form a class, and fuzziness results from the inexact relationship between an instance (i.e. sample) and the class from which it is created. An object belongs to a class with a degree of membership. Fuzzy attributes are multivalued attributes which can have a set of values connected with logical operators: AND: < …>, OR: {…}, XOR: […]. AND Semantics requires an attribute to take more than one value and all values exist simultaneously; OR semantics allows an attribute to take more than one value, all or some of which may exist simultaneously; XOR (Exclusive OR) semantics dictate that exactly one of the object attribute values is true. The definition of the NDVI_Climate class with fuzzy attributes is presented below: CLASS NDVI_Climate PROPERTIES ImageID; LocationID; Greenness; LandCoverType; AvgSoilTmp; TotalPrecipitation; TotalEvapotranspiration; END

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The ImageID is used to retrieve the image with the temporal information such as year and month and also the images stored in the image database, and LocationID is used to retrieve the spatial information such as longitude and latitude. The domains for each attribute are defined as: dom(Greenness) = [VeryPoor, Poor, Normal, Good, VeryGood] dom(LandCoverType) = {SmallGrains_RawCrop, Cropland_Woodland, Cropland_Grassland, Grassland, Shrubland, Forest, Irrigated_Agriculture} dom(AvgSoilTmp) = [VeryLow, Low, Normal, High, VeryHigh] dom(TotalPrecipitation) = [VeryLittle, Little, Normal, Much, VeryMuch] dom(TotalEvapotranspiration) = [VeryLittle, Little, Normal, Much, VeryMuch] “Every class has a range definition for each of the fuzzy attributes with corresponding relevance rules indicating the importance of that attribute in the definition of that class. An attribute of a class is allowed to take any value from the domain without considering the range values”11. In this study, for example, the range for ABNORMAL_NDVI class representing the abnormality NDVI under normal or good climate condition is defined as follows: rngABNOR(Greenness) = [VeryPoor, Poor] rngABNOR (LandCoverType) = {SmallGrains_RawCrop, Cropland_Woodland, Cropland_Grassland, Grassland, Shrubland, Forest, Irrigated_Agriculture} rngABNOR (AvgSoilTmp) = [Normal, High] rngABNOR (TotalPrecipitation) = [Normal, Much] rngABNOR (TotalEvapotranspiration) = [Normal] Relevance weights are assigned for each attribute as follows: RLV(Greenness) = 3 RLV(LandCoverType) = 2 RLV(AvgSoilTmp) = 2 RLV(TotalPrecipitation) = 1 RLV(TotalEvapotranspiration) =1 The more similar an object’s attribute value to the range definitions, the higher the class/object membership degree. The membership degree of the object O j to class C is determined using11:

µc ( o j ) =

∑ INC ( rng c ( ai ) / o j ( ai )) * RLV ( ai , C )

(2)

∑ RLV ( ai , C )

where INC ( rng c ( ai ) / o j ( ai )) is the value of the inclusion taking into account the semantics of multivalued attribute values,

RLV ( ai , C ) is the relevance of attribute ai to the class C and is given in the class definition. The weighted-average is used to calculate the membership degree proportional to their relevance.

4.2. Membership Function and Similarity Matrix The membership functions are used to calculate the membership degree of crisp values to determine the corresponding fuzzy set that they belong to at attribute level. For example, the following normal distribution function, which is a time extension version of the function shown in Ref. 11, is used for the fuzzy attribute AvgSoilTmp:

 1 x −m−t 2       2  σ   

µ D ( x ) = exp −

(3)

where x is the crisp soil temperature (e.g. 65 oF at 4 inch), m is the central temperature, σ is the spread or the standard deviation of the fuzzy term, t is the offset temperature value corresponding to the different month, and µ is the membership value of x to the fuzzy domain D . In this study of mid-growing seasons (April-September), the numerical domain of soil temperature is taken from 32 oF to 104 oF. The m and σ values are given in Table 3 and the offset values are given in Table 4.

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Table 3. m and σ values for AvgSoilTmp domain.

σ (oF)

m (oF) VeryLow

32

13.5

Low

50

13.5

Normal

68

13.5

High

86

13.5

VeryHigh

104

13.5

Table 4. The offset temperature values.

t (oF) April

-9

May

-4.5

June

0

July

9

August

13.5

September

9

Given the following definition of similarity relationships in Ref. 12,

s ( x , x ) = 1 ( reflexivity ) s ( x , y ) = s ( y , x ) ( symmetry )

(4)

s ( x , y ) ≤ max z∈D [min( s ( x , z ), s ( z , y ))] (transitivity ) the similarity matrix of the LandCoverType attribute which has seven possible values: SmallGrains_RawCrop (SG_RC), Cropland_Woodland (CL_WL), Cropland_Grassland (CL_GL), Grassland (GL), Shrubland (SL), Forest (F), Irrigated_Agriculture (IA) is shown in Table 5. Table 5. Similarity relation of LandCoverType.

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SG_RC

CL_WL

CL_GL

GL

SL

F

IA

SG_RC

1.0

0.8

0.7

0.6

0.3

0.2

0.1

CL_WL

0.8

1.0

0.7

0.8

0.3

0.2

0.1

CL_GL

0.7

0.7

1.0

0.8

0.3

0.2

0.1

GL

0.6

0.8

0.8

1.0

0.3

0.2

0.1

SL

0.3

0.3

0.3

0.3

1.0

0.2

0.1

F

0.2

0.2

0.2

0.2

0.2

1.0

0.1

IA

0.1

0.1

0.1

0.1

0.1

0.1

1.0

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5. IMAGE MINING EXPERIMENT Two image mining tasks were studied. The first task was to identify possible abnormality in vegetation condition and relate it to the climatic variables. The second task was to find similar time sequence patterns in the growing season, with user specified fuzzy queries.

5.1. Abnormality Detection • • •

Mining task: To find locations with the abnormality “greenness” related to the climatic variables. Domain knowledge: Plant growth rate is often proportional to temperature.20 Likewise, strong correlation between NDVI and soil temperature is reasonable because soil temperature affects plant germination, growth, nutritional behavior, and productivity. 8,9 Example query: Given the land cover is mainly small grains and raw crop, to find “very poor” vegetation condition with “high” average soil temperature.

The adjectives “very poor” and “high” are fuzzy predicates to query the objects in the FOODB. The corresponding images are retrieved from the image database and shown as an image list. Users can click the label of each image to show detail information such as location and date.

Figure 4. Example of abnormal NDVI detection.

5.2. Similar Time Sequences Detection • • •

Mining task: To find similar growing patterns related to precipitation at different locations in different years. Domain knowledge: There is a time lag associated with the NDVI response to precipitation events, and the length of the lags varied with the amount of precipitation.8 The lag is believed to reflect the time interval between a precipitation event and the time when precipitated water reaches plant roots and affects plant growth.9 Example query: Find the time sequence vegetation index similar to the selected sequence, covering the whole midgrowing season.

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The user first browses the image database to select a time sequence images at an interesting location with a mixed land cover type, e.g. cropland and grassland. Then, the image sequences with the similar growing pattern of such a type at different location and during different mid-growing season are retrieved.

Figure 5. Example of similar time sequences detection.

6. SUMMARY AND FUTURE WORK We have presented a specific KDD process in the field of remote sensing, which is a potentially promising application area of data mining. Based on the fuzzy object-oriented database model, a practical spatio-temporal image mining database with fuzzy features was developed, and applied to some specific mining tasks. Our future work will consider including other important databases such as the State Soil Geographic (STATSGO) database, which consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape, and can be cartographically shown at the scale mapped. It could be used to better interpret some interesting mined patterns related to the soil characteristics. We will also look at some higher resolution remote sensing images and radar images. A software infrastructure integrating the feature extraction, image storage and retrieval, fuzzy query, visualization and report tool with friendly user interface is under development, which aims to be an intelligent assistant for remote sensing image analysts.

ACKNOWLEDGEMENTS This project is supported by the Nebraska Research Initiative. The authors are thankful to Professor Bill P. Buckles at Tulane University, Professor Adnan Yazici at Middle East Technical University, and Dr. Roy George at AT&T Laboratories for assistance in the development of the fuzzy object-oriented database. We also acknowledge the help from Dr. Kenneth G. Hubbard and Jim Lacy at UNL for providing the data sets, and Lei Ji at UNL for assistance in data preprocessing.

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