An automated object-based classification approach ...

An automated object-based classification approach for Updating Corine Land Cover data Thilo Wehrmanna,c , Stefan Decha,c , R¨udiger Glaserb a DLR–DFD

German Remote Sensing Data Center, Weßling, Germany; of W¨urzburg, Remote Sensing Unit, Am Hubland, W¨urzburg, Germany; b University of Heidelberg, Department of Geography, Im Neuenheimer Feld, Heidelberg, Germany c University

ABSTRACT In this paper, the framework of an object-based classification approach for land cover and land use classes is presented. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. Remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique – a rather new trend in image classification – with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification. Keywords: Object-Based Image Classification, Automatisation, CORINE Land Cover, Pattern Recognition, Machine Learning, Human Image Understanding

1. INTRODUCTION In this paper we present an automated approach for land cover classification. It is modelled on human image understanding and combines the object-based classification with fuzzy a-priori knowledge about separate land cover classes depending on landscape units. The development of this approach is necessary because it lacks existing methods to handle more complex classification tasks like extracting CORINE classes. Land cover is defined as the observed (bio-) physical layer, which covers the surface of the Earth. 1 However Land use implies the man-made function of the land. There is still an increasing demand for accurate LandCover / LandUse (LC/LU) information in several institutions and administrations for planning and science (e.g. input data for European structural development programmes and INSPIRE – INfrastructure for SPatial InfoRmation). Frequently lacking these spatial data, users are forced to use statistical data summarized to administrative levels for monitoring and modelling. In many cases even statistical data does not provide all information needed, e.g. knowledge about impervious areas. 2 Therefore accurate, detailed and spatially derived data is crucial to use, visualize and analyse changes in LC/LU. Remote sensing is the only alternative to acquire spatially contiguous and homogeneous data in various geometric, spectral and temporal resolutions. However, the effort of LC/LU classication by visual image interpretation is too high. Therefore it is far too expensive to produce large scale land use maps from remotely sensed data. Alternatively, operational classication can convert continuous image data into thematic information, producing LC/LU classes to its best level of detail. This initial classication assists human interpreters by producing pre-classified LC/LU maps. Experts are able to focus upon more complex topics. Further author information: (Send correspondence to Thilo Wehrmann) Thilo Wehrmann: E-mail: [email protected], Telephone: +49 931 888 4958

Classification The act of forming into a class or classes; a distribution into groups, as classes, orders, families, etc., according to some common relations or affinities. W EBSTER ’ S 1913 D ICTIONARY In this context, image classification is defined as extraction of differentiated classes of land use and land cover categories from remotely sensed satellite data due to common spectral, textural or contextural features. L ILLESAND AND K IEFER 3 distinguish three types of image information, including spectral, spatial and temporal patterns. The analysis of spectral patterns is widely implemented in commercial software packages. Its classification algorithms use statistical clustering, assigning pixels to classes according to its spectral information (per-pixel classification). However, the relation of pixels to its neighbours, utilizing various measurements such as texture or entropy, is often an important characteristic for class assignment and incorporates the spatial pattern into image classification process. If multitemporal data is available the temporal patterns, e.g. phenology and periodical changes, can be used to aid feature identification. Common automated classification approaches such as unsupervised classification categorize spectral data into statistical distinct clusters (ISODATA, k−means). Afterwards the human interpreter assigns these clusters to thematic land cover classes. Another approach, the supervised image classification, employs representative sample sites of known land cover classes as training areas. Although these training sites refer to real land cover classes, additional information such as background knowledge or topographic maps is helpful for accurate class assignment. Due to the process that every pixel must be considered, pixels of unknown land cover are categorized to their most likely land cover class (for example depending on probability (Maximum Liklihood) or closest distance to the mean in feature space (Nearest Neighboor)). The pixel-based classification approaches do not allow to determine land use classes, because the satellite cannot observe anthropogenic functions of an area. A solution to this problem is either the comprehension of more information extracted from the data source (e.g. topology) or auxiliary information. According to the geometrical resolution of the image, mixed pixels cause further problems, because only objects larger than the geometrical resolution provide spectrally ”pure” pixels. Another categorization for classification techniques is the assumption of data distribution in feature space. The parametric approaches like Maximum Liklihood depend on a predeefind distribution function (mostly gaussian) of the data, because the mean and covariance are calculated from the trianing set. Non-parametric techniques are Artificial Neural Networks(ANNs) and Decision Tree Classifiers(DTCs). They reconstruct the data independently from statistical distribution. Another method for non-parametric image classification is the Support Vector Classifier (SVC) based on Structural Risk Minimization4 (SRM) developed by Vapnik. A rather new approach is the object-based classification, integrating spatial and spectral patterns. The image is spatially segmented into homogeneous areas, called image objects, 5 prior to the actual classification step. This procedure generates additional information about the object, e.g. shape, size and neighbourhood, which can also be integrated into image classification. Features and attributes of classes can be inherited to single objects in the class hierarchy. The commercial software eCognition6 uses this per-field approach . Utilising objects instead of pixels allows the determination of certain land use classes based on object size and neighbourhood relationships for the first time in classification. pixel bases techniques

parametric

non-parametric

object based techniques

Maximum Likelihood (ML) Nearest Neighbour (NN) ... Artificial Neural Networks (ANN) Decision Tree Classifier (DTC) Support Vector Classifier (SVC) eCognition experimental approaches

Table 1. Frequently used supervised Classification Approaches

There are many automated or semiautomated approaches for classification of remotly sensed data. They use low spatial / high temporal resolution data (AVHRR,7 MODIS, VEGETATION8) for land cover classification or specify and therefore simplify the classification scheme for just a few classes (e.g. roads, 9 junctions, vehicles, sealed areas). CORINE Land Cover Project The CORINE LandCover (CLC) project develops a pan-european land cover database based on remote sensing data. Its hierarchical structure contains 44 different but harmonized land cover classes grouped into three major categories. The first data acquisition was in the mid 90ies. Currently the CLC2000 update 10 is nearly completed. The information is derived manually by GIS-aided visual image interpretation with tremendous financial and human effort. Its scheduled update interval is ten years. The hierarchical structure of the CORINE classes allows logical class aggregation, hence abstract mapping. The classification system is extendible by adding classes to level four and five. The pan-european CLC project requires partial operationalisation of the classification process. This operational classification chain enables faster and more frequent update as well as improved class information. The proposed classification system builds upon two different datasets (CLC90 and CLC2000) for training and validation purposes. There is several research done in CORINE Land Cover data related to class conversation 11 and updating. Besides the CORINE system there are more land cover classification systems developed by non-european agencies (e.g. FAO, USGS).

Figure 1. Research Area: The subscene covers an area more than 5.000 km2 of heterogeneous classes (NDVI; edge-enhanced)

2. RESEARCH AREA AND DATA SETS There are several testing sites randomly choosen over entire Germany. These sites allow to test the approach with different class composites. The current research area (Figure 1) is in the north-eastern part of Germany. It includes the capital,Berlin, and extends to the Odra river in the East and the Lake Plateau of Mecklenburg in the North (Mecklenburg-Western Pomerania). It is part of the North German Plain, which was heavily reshaped by nordic ice sheets during the Pleistocene. Due to the heterogenity of the landscape (younger glacial drift areas) and the high degree of anthropogenic functionality and

usage (urban settlements, river management, agriculture, local recreation) it is suitable for testing of automated classification approach. The area covers several natural landscape classes (ground moraine plates with lakes / depressions, terminal moraines, outwash plains and glacial valleys) with different dominant land cover classes. The satellite data used for this study was acquired on July, 7th 1989 by Landsat 5 TM (path 193 / row 23). The multispectral sensor collects 6 bands in 25m spatial resolution (bands 1–5, 7) and one band (band 6) in 120m resolution. Due to georeferencing and further processing, all bands and data are geometrically resampled to 28.5m resolution. The Natural landscape classes (”Naturr¨amliche Gliederung”) by Meynen-Schmith¨usen are digitized and rasterized for select actual training samples. Moreover a Digital Elevation Model (DEM) is used for terrain analysis and visualisation.

3. METHODOLOGY Remotely sensed image classification is a well examined field of research. However no approach is omnipotent nor capable to fulfill human generalisation abilities. But, is it possible to imitate human pattern recognition ? It is not trivial to answer the question and leads us towards neuropsychology. Following the human image understanding in a more psychological way, the task would be split in several parts or processing stages. In the mid-80s B IEDERMAN 12 developed a theory to explain human image understanding through Recognition by Components (RBC). The problem of object recognition is divided into several cascading subprocesses. 1. Edge Extraction (a) Detection of Nonaccidential Properties (b) Parsing Regions of Concavity 2. Determination of Components 3. Matching of Components to Object Representations 4. Object Identification When an image of an object is illustrated on the retina, the RBC approach assumes that the representation of the image is segmented – or parsed – into separate regions. The object is decomposed into primitive components also known as geons (for ”geometrical ions”). Instead of a vast account of these primitives further research determined ”only” 36 different geons. Any complex objects can be constructed by these 36 geons. Color and texture are only used for additional information about the object or for segmentation. Two dimensional and three dimensional objects can be represented by its components and therefore classified by its gestalt. The robust capacity of abstraction allows the recognition of various incomplete and degraded objects. Instead of recognizing daily life objects, the classification of remotely sensed images is a much more specialized problem. Because of sensor limitations ”pure” objects cannot be recognized from space. There have to be other features in the process of class identification. The RBC-approach of B IEDERMAN bears how the human speech is perceived. The understanding of the human speech is also a problem of object recongition. Complex object arrangements can be abstracted into a small number of primitives. ”Only” 44 phonems or 26 letters are sufficient to code english speech or english words, respectively. The set of primitives is derived from dichotomous or trichotomous contrasts of a small number. With simple combination of primitives and dichotomous contrasts it is possible to code a wide range of words. E DELMAN 13 has recently explained the representation of objects by reference shapes, or ”prototypes” (Chorus of Prototypes). He introduced a global shape space in which all kinds of objects can be projected and classified. But robustness and processing speed could not be achieved by using only object prototypes. R IESENHUBER 14 developed a more neuropsychological way of object recognition. He broadened the idea of object representation by establishing class categories (Categorical Perception).

The next example explains some of these features in context of specialised land cover classification. Once upon a midnight dreary, while I pondered weak and weary, Over many a quaint and curious volume of forgotten lore, While I nodded, nearly napping, suddenly there came a tapping, As of some one gently rapping, rapping at my chamber door. ‘’Tis some visitor,’ I muttered, ‘tapping at my chamber door — Only this, and nothing more.’ E.A.P OE , T HE R AVEN (1835) In the traditional way of classification the former paragraph is split into separate letters. These letters and the occurence of them is statistically analysed. It is not a trivial work to get the distribution of this data in any feature space. Indead the results can be compared to other poems and maybe the author used a special vocabulary we can use to identify the text. An extended approach is the use of words – devided by spaces and word marks because the combination of the letters is not randomly distributed and follows more or less rigorous rules. These letter fragments can be evaluated, too. At that time words get a special function (as nouns, verbs, attributes, etc.) and meaning in the sentence. The classification is more accurate than just using letters. However the number and type of words do not describe the nature of the author. Maybe it is possible to determine the author with this method but there is a certain source of mistake in the results. Letters and words do not allow to estimate the author exactly. A possible solution is the extension of used features. Certain words (e.g. dreary, weak, forgotten lore, muttered, etc.) get additional information about their meaning. The context exists from the meaning of the separate words. This information is extracted from learning. Words become objects with content and additional attributes. These objects derive from classes which inherit further attributes. With this information the poem obtains new features (like feeling / atmosphere). This new feature is more stable and capable to classify the poem because instead of using the same letters or words Poe used the same atmosphere or style for most of his work. In using categories these features can be used to estimate the right category for the poem. It is obvious that there are several information levels in these patterns. Letters and phonems (in speech) are the lowest information units. The next level consists of words with semantic meaning. These words are coded by letters and enable the exchange of information (communication). The sentence explains a short statement. The content of information exists at that level. More information is given in an accumulation of sentences (paragraph). The document itself imparts complex structures, too. Level 1. 2. 3. 4. 5.

Text Letters Words Sentences Paragraphs Documents

– – – – –

Image Pixel Image Objects Forest Objects Forest Class Landscape

Table 2. A comparison between text and image objects

What can we learn from this example for image classification? For object classification it is highly important to choose the right features. Pixels and image objects reflect the true but simple nature of the objects in the image but they cannot be used alone to fulfill complex classification tasks. The human interpret solves this problem in implementing his background knowledge into the classification (e.g. class scheme, hierarchical classification and so on). These results become better but it lacks of spatial or thematical transferability of the working steps. What kind of features can be generated from the image data we can use for background knowledge (a-priori knowledge) ? Surely it depends on the nature of the classes. For the CORINE Land Cover classification detailed information about vegetation, soil and artificial structures are useful for hierarchical classification. Moreover a certain knowledge about the spatial and temporal distribution of the classes enhances the classification accuracy. Single objects are not randomly distributed in space and time. In a simple way it is possible to create rules for CORINE objects. The combination of the hard (pixel data) and soft (a-priori knowledge) information is realised in a fuzzy manner.

The combination of object-based classification with a-priori knowledge is not a new invention. Especially the use of eCognition allows a complex realisation for implementing knowledge. However the developed class hierarchies are rarely transferable without ease. The robustness and stability of the system evolve from using simple features and non complex rules. Most classes from CORINE LandCover can be identified in combination with former CORINE data set (CLC90) with satisfying accuracy. The classification can be enhanced with additional information and multitemporal data.

ImageLevel

SemanticLevel

Level pixel level

extracted from spectral pixel data

segment level

spectral pixel data shape

object level

shape

class level

segments objects

landscape level

classes, objects

Information type tone brightness, saturation texture (kernel size) texture (adapted) size (absolute) compactness, ... size (relative) compactness, ... pattern of segments pattern of objects spatial distribution neighbourhood connectivity spatial distribution diversity

Table 3. Information Levels of Images

The process of CORINE object recognition of remotely sensed data is also separated into several processing stages (data preparation, generalisation, post-processing). 1. Preprocessing 2. Multi–Level Classification / Generalisation 3. Post-processing These stages are adapted to the conceptual frame for image understanding by I BRAHIM. 15 The model consists of filtering, segmentation, lower image interpretation and complex object recognition. It is postulated that image understanding is a knowledge-based process. Knowledge can be coded implicitly (procedural knowledge) or explicitly (declarative knowledge). Procedural knowledge is incorporated in an algorithm or a program. However declarative knowledge is represented symbolically in production rules, semantic nets and frames. In contrast to procedural knowledge the explicite representation does not contain any control information about the usage of the knowledge.

3.1. Preprocessing All preprocessing modules are developed in an object-oriented way and use relational databases for data storage. Metadata Extraction This data is extracted from the scene (acquisition date and place and sensor type). The geographical position determines the Natural landscape classes of the scene. Image Enhancement The data mining approach (SVC) classifies the data spectrally in a supervised way. This attempt requires some sort of training data, which is stored in a training samples database. To enhance classification results an atmospheric correction tool like ATCOR16 is used to homogenise data from various scenes. In using reflectances instead of simple DNs it is possible to apply ratios and colour features for a-priori knowledge.

Image Segmentation There are different approaches for image segmentation. Until now the fractal net evolution approach (FNEA) is used to segment the data. This approach is embedded in the commercial eCognition 6 package. It is planned to substitute this method for an own-developed, more automatical texture approach. Generation of a-priori knowledge Information is extracted from the image with a-priori knowledge (e.g. Vegetation mask derived from NDVI, Greeness and NIR channel). Thus it is possible to gain fuzzy LC masks for vegetation, soil and settlement.17

Level 0

Non Vegetation

Mixed Vegetation

Dense Vegetation

Level 1

Waterbodies Impervious Areas Bare Soil Permanent Snow / Ice

Settlement areas Cropland Artificial areas Natural areas

Forest Meadow Cropland Natural areas

Level 2

Clear Water Disturbed Water Commercial / Industrial site Dense residential sites Acres, Bare Soil Rock, Exploited Areas Sparse Vegetation Permanent Snow / Ice

Open Residential Sites Cropland (early stages) Cropland (extensive usage) Parks Greenland (early) Wetlands

Dedicuous Forest Coniferous Forest Meadow Cropland (later stages) Parks Greenland (later) Wetlands

Figure 2. Example for a Vegetation Memership Function

3.2. Multi-Level Classification Due to the different information levels (table 3) a multi-level classification system is developed. It combines the objectbased classification technique, machine learning capacities and fuzzy a-priori knowledge decision in an iterative classification and identification process. Generalisation is split in two parts (spatial and thematical) to ensure robustness and accuracy. The spatial generalisation is done by image segmentation. Whereas the thematical generalisation consists of hierarchical spectral classification and fuzzy land cover masks. Level Level 0/1 Level 2 Level 3 Level 4

Component hierarchical, per pixel Classification with Support Vector Classifier Neighbourhood / Shape Analysis with GIS Tools Temporal Analysis (Change Detection) with Decision Support System CORINE Analysis with Decision Support System Table 4. Multi-level Generalisation / Classification

Figure 3. Concept for CORINE object recognition

Object classification A spectral database provides information to identify training samples in the image. A new promising18 data mining technique, called Support Vector Classifier (SVC), is used for supervised classification. It is a machinelearning based approach developed for classification problems and regression analysis. Contrary to Maximum Likelihood Classication (MLC) the SVC method is non-parametric and produces higher-generalized results. SVCs are successfully applied to broad aspects of classification like particle identification, face identication and text categorization. The approach is systematic and reproducible. Although it is a new classification approach, SVCs are used for remotely sensed data for several times1920 .21 The modules allocate the training samples in ”intelligent” way (sensor, phenology, geographical position etc.) from the database. Support Vector Classifier The technique of support vector machines (SVM) has been developed in the framework of statistical learning approaches. In B ENNETT AND C AMPBELL 22 the approach is examined geometrically because “it is a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees and practical algorithms meet.” VAPNIK 4 gives an excellent comprehension in statistical learning theory. Like in other binary classification tasks, there are m data points xi (i=1, ..., m) having corresponding labels yi = ±1. The data points are represented in a k-dimensional feature space. Let the classification function be: f (x) = sign(wx − b). The vector w determines the orientation of a discriminant plane, and the scalar b determines the offset of the plane from the origin. Let us consider the two data sets are linearly separable. There are infinitely many possible planes that correctly separate the two training sets. The optimal plane with the highest degree of generalization is the one being ”furthest” from both clusters. This plane can be constructed by maximizing the margin between both classes. For that purpose those points have to be found with the closest distance to each other. There are two ways to determing these points. One of them is to create a convex hull around each training data set. The best plane will bisect orthogonally the closest points in the convex hulls. These located points are called ”support vectors”. The second method is to maximize the margin between the parallel planes which limit the data of each class. After rescaling the data the linear function w · x i − b ≥ 1 defines all data of the

Figure 4. Schematic overview about Support Vector Machine

class +1. For the class -1 the similar function is w · xi − b ≤ 1. The best dividing plane between the two dataset can be calculated in maximizing the distance between these support planes (w · x = b + 1 and w · x = b − 1). The margin is γ = 2/ k w k2 . Maximizing the margin is equivalent to minimizing γ. The method of maximizing the margin between the supporting planes is equivalent to the first one because the support vectors on the plane are the same as the closest points on the convex hull (duality in mathematics). The solution of the problem depends only on these support vectors instead of the whole data. In maximizing the margin of separation the complexity is reduced to describe a linear function. This produces less generalization errors and improved generalization with better probability. Thus the dimensionality of the data does not affect the size of the margin. In contrast to other classification methods the problem of overfitting high-dimensional data (”curse of high dimensionality”) is reduced. The more complex the function describing both datasets the poorer the capability of future generalization. Maximizing the margin reduces the complexity of the model. In reality datasets with linear separability are rarely met. With real life data the strategy of construction a separating plane will produce no solution. To estimate the best (reduced) convex hull the influence of each point has to be restricted in introducing an upper bound D < 1 on the multiplier for that point. The former equitation has to be enhanced with the idea of reduced a convex hull. The separability of the maximized margin can produce a wide range of training errors if the linear discriminants are not adapted to the data sets. This problem can be solved by introducing the kernel trick. Kernel Trickery In many cases a simple linear discriminant function cannot solve each classification problem (for instance no linear function can describe a quadratic function).Additional features enhance the data set to increase the dimensionality in using a kernel function. In this artificial feature space a linear discriminant can be constructed. This kernel mapping can be applied to all equations. The SVM approach has developed several kernel functions. θ(u) Degree d polynomial Radial Basis Function Machine Two-Layer Neural Network

K(u, v) (u  · v + 1)d

exp

ku−vk2 2σ

sigmoid(η(u · v) + c)

Table 5. Some Kernel Functions

Kernel mapping produces highly nonlinear classifiers using the same linear discriminant function. Up to this step there are only image objects containing the spatial and spectral information of the individual pixels. Further useful knowledge can be calculated, e.g. information about shape, size or texture parameters. The integration of this additional knowledge allows the construction of complex objects by aggregating the simple image objects. Fuzzy Object Analysis The classification of image objects deliveres a fuzzy set of land cover classes of different levels (rough and finer classes). The miscellaneous information levels, the land cover masks (vegetation, soil and settlement) and former classification results add additional ”diffuse” data to the object identifier. At the end the determination module combines all data sets (Spectral Membership + a-priori Membership + [Landscape] + [Terrain] = Fuzzy Land Cover Class). At that point several classification passes can alter the loading of each component (simple ”learning” element). After defuzzification the actual CORINE class is estimated. Spectral 0.6 Barren 0.2 Grassland 0.1 Settlement

Vegetation 0.4 No Vegetation 0.3 Mixed Vegetation 0.6 Mixed Vegetation

Settlement 0.2 Settlement 0.8 Settlement 0.9 Settlement

Soil 0.7 Soil 0.3 Soil 0.1 Soil

=⇒ 211 Non-irrigated arable land Table 6. Example for non standardised membership and following defuzzification process

3.3. Post-processing CORINE classes are clearly described thematically and geometrically. Image objects which are smaller than 25 ha (0.25 km2 ) are eliminated or rather merged with their neighbours. In many cases (no change in objects) the border lines can be used from current CORINE data because of redundancy / validation / change detection. Complex classes (e.g. airports) can be constructed in merging certain objects together (asphaltic areas, greenland and buildings). The validation is effected by current CORINE classification data (CLC 2000). Employing this data, there are three possibilities of temporal variation. Due to temporal reasons, the (1) geometry changed because of processes such as urbanization and / or (2) the thematic content has been altered, e.g. afforestation. However, the most likely assumption for many CORINE objects is (3) no change. Misclassified objects can be manually corrected after notification. It is very practical to incorporate the existing CORINE data into the classification system. The supervised classification approach for classifying image objects requires training samples. These samples must be extracted from the image. Previous CORINE datasets can also help to assign thematic knowledge to this training data without manual interaction. For CORINE purpose it is also necessary to use the same geometry for comparison and change detection.

4. RESULTS AND CONCLUSION Not all componends of this approach are implemented in Python until now. Up to this moment it is possible to process the a-priori knowledge masks for the subscene. Currently level 2 to 4 of the multi-level classification scheme is in progress. Until the end of the year the remaining componends will be implemented. An extensive testing will validate the project. The approach uses the full geometrical and multispectral potential of LANDSAT 7. It is possible to extract objects smaller than 25 ha for certain classes (one disadvantage of current CORINE classification). The (semi-) automated method enables timely land cover maps for large scale areas in spite of heavy processing costs.

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