Statistical Feature Extraction from SAR Images

Transcription

1 Statistical Feature Extraction from SAR Images (Lecture I- Tuesday 22 December 2015) Training Course on Radar Remote Sensing and Image Processing December 2015, Karachi, Pakistan Organizers: IST & ISNET Parviz Tarikhi, PhD Alborz Space Center, ISA, Iran

2 Outline Introduction Object-based Image Analysis Remote Sensing Data Classification Texture 12:21:40 PM 2

3 (JARS 1993) Image Enhancement & Feature Extraction: conversion of the image quality to a better and more understandable level for feature extraction or image interpretation; while radiometric correction is to reconstruct the physically calibrated value from the observed data. Feature extraction: operation to quantify the image quality through various parameters or functions applied to the original image. 12:21:40 PM 3

4 (JARS 1993) Conversion of the image data 12:21:40 PM 4

5 (JARS 1993) applied mainly for image interpretation in the form of an image output Conversion of the image data 12:21:40 PM 5

6 (JARS 1993) applied mainly for image interpretation in the form of an image output Conversion of the image data normally used for automated classification or analysis in a quantitative form 12:21:40 PM 6

7 (JARS 1993) Conversion of the image data 12:21:40 PM 7

8 (JARS 1993) Conversion of the image data includes: gray scale conversion, histogram conversion, color composition, color conversion between RGB and HSI, etc., are usually applied to the image output for image interpretation. 12:21:40 PM 8

9 (JARS 1993) Conversion of the image data includes: gray scale conversion, histogram conversion, color composition, color conversion between RGB and HSI, etc., are usually applied to the image output for image interpretation. involves: - Spectral features special color or tone, gradient, spectral parameter etc. - Geometric features edge, lineament, shape, size, etc. - Textural features pattern, spatial frequency, homogeneity, etc. 12:21:40 PM 9

10 (JARS 1993) Examples of feature extraction 12:21:40 PM 10

11 (Murai S 1998) Feature Extraction is the operation to extract various image features for identifying or interpreting meaningful physical objects from images. Features are classified into three types. Spectral features: color, tone, ratio, spectral index etc. Principle components and normalized vegetation index are widely used. Geometric features: edges, lineaments etc. Spatial filtering of edge detection is commonly used to extract linear features such as roads, geological lineaments, boundaries of agricultural fields etc. Generally spatial filtering is applied as follows. Step 1: smoothing filter such as mean or median is applied to avoid high frequency noises. Step 2: edge detection filter such as Sobel, Laplacian or High-pass is applied to detect edges. Step 3: line edges are detected by thinning and sometimes edge closing. Textural features: pattern, homogeneity, spatial frequency, etc. 12:21:40 PM 11

12 12:21:40 PM 12 (Murai S 1998)

13 Object-based Image Analysis 12:21:40 PM 13

14 Object-based Image Analysis Introduction Principles of Object formation Segmentation Approaches Top-Down approaches Bottom-Up approaches Reshaping Algorithms Object features Principles of OO-Image Classification Fuzzy Logic Membership Functions 12:21:40 PM 14

15 SAR-EDU, Object-based Image Analysis Introduction Recent advancements in the availability of higher spatial resolutions of remote sensing data requires new analysis aproaches. Lake Not only pixel-based algorithms can be incorporated but also the color and shape of areas as well as neighborhood and hierarchy infomation must be considered. Object-based approaches seek for the recognition of the color and the shape of associated areas in order to derive natural objects and assign semantic classes to them. 12:21:40 PM 15

16 SAR-EDU, Object-based Image Analysis Introduction Recognizing Colors Forest Lake Recognizing Shapes Deriving natural objects Agriculture Classification Data Source: USGS 12:21:40 PM 16

17 (Trimble ecognition 8.7 User Guide) Object-based Image Analysis Object Formation The process of partitioning a scene (a remote sensing image for example) into nonoverlapping regions (segments) in scene space (e.g., image space). (Schiewe 2002) An image object: a group of pixels in a map Each object represents a definite space within a scene and objects can provide information about this space. The hierarchy of image objects 12:21:40 PM 17

18 SAR-EDU, Top-Down Object-based Image Analysis Image Segmentation Approaches An Image Segmentation is the process of completely partitioning a scene (e.g., a remote sensing image) into non-overlapping regions (segments) in scene space (e.g., image space) SCHIEWE (2002). Hereby, the image is split in homogeneous image object primitives. In contrast to pixel-based clustering approaches spatial neighborhood relations are considered in addition to the spectral information. In the process of image segmentation there are different approaches of object formation. Top-Down Approaches: The image is subdivided into smaller NUSSBAUM image & MENZ (2008) objects by means of a certain algorithm Bottom-Up Approaches: Starting from the smalles image object primitives (i.e. pixels) the segments are composed of multiple pixels with similar properties Reshaping Approaches: Can be considered as secondary object formation approaches, since the image objects are mostly manipulated based on an a priori classification The following slides are presenting some examples of segmentation approaches. 12:21:40 PM 18

19 Top-Down (Nussbaum & Menz 2008) Object-based Image Analysis Image Segmentation Approaches Image Level Bottom-Up Object Level Pixel Level Examples: Top-Down Chessbord Quadtree Bottom-Up Mulit-Resolution Mutli-Threshold Reshaping Merge Region Grow Region 12:21:40 PM 19

20 Top-Down (Nussbaum & Menz 2008) Object-based Image Analysis Image Segmentation Approaches The image is subdivided into smaller image objects by means of a certain algorithm. Bottom-Up Image Level Object Level Pixel Level Examples: Top-Down Chessbord Quadtree Bottom-Up Mulit-Resolution Mutli-Threshold Reshaping Merge Region Grow Region 12:21:40 PM 20

21 Top-Down (Nussbaum & Menz 2008) Object-based Image Analysis Image Segmentation Approaches Starting from the The image is subdivided into smaller image objects by means of a certain algorithm. Bottom-Up smallest image object (say pixels) the segments are composed of multiple pixels with similar properties. Image Level Object Level Pixel Level Examples: Top-Down Chessbord Quadtree Bottom-Up Mulit-Resolution Mutli-Threshold Reshaping Merge Region Grow Region 12:21:40 PM 21

22 Top-Down (Nussbaum & Menz 2008) Image Segmentation Approaches Starting from the The image is subdivided into smaller image objects by means of a certain algorithm. Bottom-Up smallest image object (say pixels) the segments are composed of multiple pixels with similar properties. Object-based Image Analysis Being considered as secondary object formation approaches, since Image Level the image objects are mostly manipulated based on an a priori classification. Object Level Pixel Level Examples: Top-Down Chessbord Quadtree Bottom-Up Mulit-Resolution Mutli-Threshold Reshaping Merge Region Grow Region 12:21:40 PM 22

23 Top-Down (Nussbaum & Menz 2008) Object-based Image Analysis Image Segmentation Approaches Image Level Bottom-Up Object Level Pixel Level Examples: Top-Down Chessbord Quadtree Bottom-Up Mulit-Resolution Mutli-Threshold Reshaping Merge Region Grow Region 12:21:40 PM 23

24 (Trimble ecognition 8.7 User Guide) Top-Down Bottom-Up Object-based Image Analysis Chessbord Segmentation - Divides image into equal squares of a given size Scale: 10 It is the simplest segmentation algorithm. It cuts the scene or in more complicated rule sets the dedicated image objects into equal squares of a given size. 12:21:40 PM 24

25 (Trimble ecognition 8.7 User Guide) Top-Down Object-based Image Analysis Bottom-Up Quad-tree Segmentation Scale: 30 It is similar to chessboard segmentation, but creates squares of differing sizes. An upper limit of color differences within each square can be defined using Scale Parameter. After cutting an initial square grid, the quad-tree based segmentation continues as follows: Cut each square into four smaller squares if the homogeneity criterion is not met. Example: The maximal color difference within the square object is larger than the defined scale value. Repeat until the homogeneity criterion is met at each square. 12:21:40 PM 25

26 (Trimble ecognition 8.7 User Guide) Top-Down Bottom-Up Object-based Image Analysis Multi-Resolution Segmentation Scale: 10 Its algorithm consecutively merges pixels or existing image objects. Essentially, the procedure identifies single image objects of one pixel in size and merges them with their neighbors, based on relative homogeneity criteria; this homogeneity criterion is a combination of spectral and shape criteria. This calculation can be modified by modifying the scale parameter. Higher values for the scale parameter result in larger image objects, smaller values in smaller ones. With any given average size of image objects, multiresolution segmentation yields good abstraction and shaping in any application area. 12:21:40 PM 26

27 (Trimble ecognition 8.7 User Guide) Top-Down Bottom-Up Object-based Image Analysis Multi-Resolution Segmentation(ctd.) Scale: 10 The Homogeneity Criterion The homogeneity criterion of the multiresolution segmentation algorithm measures how homogeneous or heterogeneous an image object is within itself. It is calculated as a combination of the color and shape properties of the initial and resulting image objects of the intended merging. Color homogeneity is based on the standard deviation of the spectral colors. The shape homogeneity is based on the deviation of a compact (or smooth) shape. 12:21:40 PM 27

28 (Trimble ecognition 8.7 User Guide) Top-Down Bottom-Up Object-based Image Analysis Multi-Threshold Segmentation Scale: 10 Its algorithm splits the image object domain and classifies the resulting image objects based on a defined pixel value threshold. This threshold can be user-defined or can be auto-adaptive when used in combination with the Automatic Threshold algorithm. The threshold can be determined for an entire scene or for individual image objects; this determines whether it is stored in a scene variable or an object variable, dividing the selected set of pixels into two subsets so that heterogeneity is increased to a maximum. The algorithm uses a combination of histogram-based methods and the homogeneity measurement of multiresolution segmentation to calculate a threshold dividing the selected set of pixels into two subsets. 12:21:40 PM 28

29 (Trimble ecognition 8.7 User Guide) Object-based Image Analysis Reshaping Merge Region - merges all neighboring image objects of a class into one large object The class to be merged is specified in the image object domain. Classifications are not changed; only the number of image objects is reduced. 12:21:40 PM 29

30 (Trimble ecognition 8.7 User Guide) Reshaping Grow Region Object-based Image Analysis - extends all image objects that are specified in the image object domain, and thus represent the seed image objects They are extended by neighboring image objects of defined candidate classes. For each process execution, only those candidate image objects that neighbor the seed image object before the process execution are merged into the seed objects. 12:21:40 PM 30

31 SAR-EDU, Features Object-based Image Analysis Image objects have spectral, shape, and hierarchical characteristics. These characteristic attributes are called features. Features are used as source of information to define the inclusion-or-exclusion parameters used to classify image objects. There are two major types of features: Object features are attributes of image objects, for example area Global features are not connected to an individual image object, for example the number of image objects of a certain class. 12:21:40 PM 31

32 SAR-EDU, Object-based Image Analysis Object Features: Examples Layer Values Mean Brightness Stdev Shape Roundness Rectangular Fit Stdev Position Distance to scene border X-distance to left scene border Y-distance to right scene border Standard Deviation (Layer 3) Elliptical Fit Distance to scene Border 12:21:40 PM 32

33 SAR-EDU, Object Oriented Classifiaction Concepts Object-based Image Analysis Classifiers Hard Soft True False Fuzzy Logic Membership Function 12:21:41 PM 33

34 (Baatz et al. 2004) It is a technique to replace boolean [true or false 0 or 1] transitions with continous transitions between the extreme values. It is possible to specify the degree of certainty for a certain object to belong to a certain class. The classification of an object is conducted by the maximum membership to a certain class. Fuzzy Logic µ low = 0.4 µ medium = 0.2 µ high = 0.0 Object-based Image Analysis µ low = 0.0 µ medium = 0.0 µ high = :21:41 PM 34

35 (Baatz et al. 2004) Object-based Image Analysis Membership functions Rectangular and trapezoidal membership functions on feature x to define a crisp set M(X) (red), µm (x) {0,1} and a fuzzy set A(X) (grey), µa (x) {0,1} over the feature range X (Benz et al. 2004). Membership functions serve a realisation of fuzzy classification rules. The figure presents an unambiguous assignment (red) and a fuzzy assignment (grey) of the membership to a certain class. In general, the wider the range of the memebership function the more vague the assignment of a certain object to a thematic class. 12:21:41 PM 35

36 (Trimble ecognition 8.7 User Guide) Membership functions -Form examples- Object-based Image Analysis Examples of predefined membership functions 12:21:41 PM 36

37 Remote Sensing Data Classification 12:21:41 PM 37

38 Remote Sensing Data Classification What means Classification? Parametric Classifiers Minimum-Distance-to-Mean K-Means Maximum Likelihood Non-Parametric Classifiers (K-) Nearest Neighbor Decision Trees Artificial Neural Networks Support Vector Machines 12:21:41 PM 38

39 Channel (Lillesand et al Remote Sensing Data Classification What means classification? Overall objective (automatically) categorize all pixels in an image in certain (i.e. predefined) classes or themes Thematic classification allocates pixels to classes based on functions of the spectral (or backscatter) properties IMAGE DATA SET (e.g. Five Digital Numbers per pixel) CLASSIFICATION (into a priori defined classes) MAP (Thematic representation of classes) 4 5 Schematic Classification Workflow 12:21:41 PM 39

40 SAR-EDU, Remote Sensing Data Classification Method overview Classification Computer based Interpretation Manual Photo Interpretation Supervised Unsupervised Parametric Non-Parametric Supervised Parametric Non-Parametric Unsupervised 12:21:41 PM 40

41 SAR-EDU, Algorithm based differentiation Remote Sensing Data Classification Parametric Classifiers Implying a specific statistical distribution Generally gaussian distribution Calculation statistical measurement (e.g. Standard deviation or Covariance Non-Parametric Classifiers No assumtion on the statistical distribution of the data Robust due to ability to describe numerous statistical distributions other than gaussian distribution SAR dats is usually not gaussian distributed! Non-parametric classifiers are more appropriate in Radar remote sensing 12:21:41 PM 41

42 SAR-EDU, Differentiation by training concept Remote Sensing Data Classification Unsupervised Classifiers No Training stage Purely based on the statistical distribution of the input data Supervised classifiers Employing manual traing of the data set to distinguish the desired classes 12:21:41 PM 42

43 Channel (Lillesand et al Remote Sensing Data Classification Differentiation by training concept Unsupervised vs. Supervised IMAGE DATA SET (e.g. Five Digital Numbers per pixel Urban Pixel (3,7) DN 1 Forest Water DN 2 DN 3 DN 4 DN Basic steps of classification (1) TRAINING STAGE Collect numerical data from training areas on spectral response patterns of land cover categories (2) (1) CLASSIFIACTION STAGE Forming Compare clusters each unknown of pixels pixel according to spectral to patterns; their assign spectral to most properties similar category (3) (2) OUTPUT STAGE Present results: Maps Tables GIS Data 12:21:41 PM 43

44 SAR-EDU, Attribute 2 Remote Sensing Data Classification Minimum-Distance-to-Mean Concept: Supervised Algorithm: Parametric Pros: Cons: Simple, efficient weak performance for classes with high variance Minimum-Distance-to-Mean Classifier Attribute 1 Training pixel Pixel under investigation 12:21:41 PM 44

45 SAR-EDU, Remote Sensing Data Classification Minimum-Distance-to-Mean The mean of every class is derived from the training data The distance of every pixel under investigation to these classes is calculated The pixel is assigned to the class with the minimal distance to the class mean 12:21:41 PM 45

46 SAR-EDU, Attribute 2 Remote Sensing Data Classification K-Means Clustering c + Concept: Unsupervised Algorithm: Parametric b + Pros: Cons: No interaction or a priori tuning necessary Result depends on initial cluster centers Empty classes possible a + a + b + c + Cluster centers Idealized data clusters K-Means Classifier Attribute 1 12:21:41 PM 46

47 SAR-EDU, Remote Sensing Data Classification K-Means Clustering Initial random definition of starting points for each cluster (a,b,c) Number of cluster centers = Number of classes Assignment of each pixel to teh closest cluster center Recalculation of the mean of each cluster Iteration of 2. & 3. until no significant changes of the cluster centers are detected. 12:21:41 PM 47

48 SAR-EDU, Attribute 2 Remote Sensing Data Classification Maximum Likelihood Concept: Supervised Algorithm: Parametric Pros: Cons: based on multiple statisitcal parameters robust Complex Processing Equiprobability contours Training pixel Maximum Likelihood Classifier Attribute 1 Pixel under investigation 12:21:41 PM 48

49 SAR-EDU, Remote Sensing Data Classification Maximum Likelihood The Maximum Likelihood (ML) classifier evaluates the variance an covariance the assing pixels to the spectral response patterns of training classes. ML implies that the training data is ordered by a gaussian normal distribution The distribution of the training pixels is done with the mean vector and the covariance matrix. Thereby a probability density function is calculated. This function calculates the probability to belong to a certain class for every pixel by counting the distance to the center of the Equiprobability Contours. 12:21:41 PM 49

50 Band 2 SAR-EDU, Remote Sensing Data Classification (K-)Nearest Neighbor Concept: Supervised Algorithm: Non-Parametric Pros: Cons: Simple implementation Slow for many image bands strong influence of k on result k = 1; 1x K-Means Classifier Band 1 k = 5; 4x, 1x k = 30; 7x, 10x,13x Class A Class B Class C Data point under investigation 12:21:41 PM 50

51 SAR-EDU, Attribute 2 Remote Sensing Data Classification Decision Trees Concept: Supervised Algorithm: Non-Parametric Pros: Cons: - Simple Structure - Fast - Combination of data possible - Complex design phase Forest Broad-leaved Needle-leaved Vegetation No Forest Decision Tree Classifier Attribute 1 No Vegetation Water No Water Agriculture Settlement 12:21:41 PM 51

52 (Atkinson & Tatnall 1997) Remote Sensing Data Classification Artificial Neural Networks (ANN) Image Channels Concept: Hybrid (Un)-Supervised Algorithm: Non-Parametric Thematic Classes Pros: Cons: - adequate for non-linear relations - Robust & error resistent - Complex processing - Model overfitting, overtraining - Black Boy System A neural network consists of a number of interconnected nodes [ ]. ANN Classifier Each node is a simple processing element that responds to the weighted inputs it receives from other nodes. Input Units Hidden Units Output Units The arrangement of the nodes is referred to as the network architecture. (Atkinson & Tatnall 1997) 12:21:41 PM 52

53 SAR-EDU, Remote Sensing Data Classification Artificial Neural Networks (ANN) Nearest Neighbor Classifiers are classifiyng data point (i.e. Pixel) under investigation according to an assignment to the nearest training pixel vector Nearest Neighbor: The pixel is assigned to the nearest training pixel K-Nearest Neighbor: The pixel under investigation is assigned to the class, that has the majority under the k nearest neighbors of this very pixel Distance Weighted K-Nearest Neighbor: The influence on the classification result of thek nearest neighbors for a certain pixel is weighted inverse proportional to the euclid distance. 12:21:41 PM 53

54 SAR-EDU, Attribute 2 Remote Sensing Data Classification Support Vector Machines (SVM) B)? A)? C)? Concept: Supervised Algorithm: Non-Parametric Pros: Cons: - Useful for high dimensional data - White Box Approach - very flexible - long processing time - needs multiple iterations - can stay unresolved Which line divides the classes best? SVM Classifier Attribute 1 Support Vectors Training Pixel Class A Training Pixel Class B 12:21:41 PM 54

55 SAR-EDU, Remote Sensing Data Classification Support Vector Machines (SVM) Classes are assigned in an hierarchical system of rules and decisions. Binary decisions are used to assign a certain pixel to a certain class. This is done by the inheritance of properties from higher (i.e. coarser) decision levels to lower (i.e. finer) levels. Every class can be subdivided into two subclasses based on a certain criterium. The combination of different sources of information is possible in a decision tree. The design of the decision tree is completely interactive. 12:21:41 PM 55

56 SAR-EDU, Further Reading Classification LILLESAND, T.M., KIEFER, R.W. & J.W. CHIPMAN ( ): Remote Sensing and Image Interpretation. Wiley & Sons, Hobkoen. BOHLING, G. (2005): Nonparametric Classification Techniques < (Access: ) Support Vector Machines BENNET, K. & C. CAMPBELL (2000): Support Vector Machines: Hype or Hallelujah?. SIGKDD Explorations 2, ( FLETCHER, T. (2008): Support Vector Machines Explained. London: University College. London, Department of Computer Science. ( Artificial Neural Networks Remote Sensing Data Classification ATKINSON, P. M. & A. R. L. TATNALL (1997): Introduction Neural networks in remote sensing, International Journal of Remote Sensing, 18:4, ROJAS, R. (1996). Neural Networks: A Systematic Introduction. Springer, Berlin. < 12:21:41 PM 56

57 Texture 12:21:41 PM 57

58 Texture Texture Basics What is texture? The GLCM Classification of texture parameters Application examples in the context of SAR remote sensing Forest Cover Urban Footprints 12:21:41 PM 58

59 SAR-EDU, What is Texture? Texture Periodic variation of grey values in a local environment (2-D in x-y direction) Texture describes the type and characteristic of the re-occurrence of objects Texture is a feature of an area the texture of a point is not defined Texture is a context feature relying on neighboring grey values Possible features: Uniformity Density Coarseness Roughness Regularity Linearity Directionality Frequency Phase 12:21:41 PM 59

60 ESA Radar image Texture - Texture is scale dependent! Texture Micro-texture (Speckle) Texture cell ~ Resolution cell System inherent Meso-texture (Scene texture) Texture cell > Resolution cell E.g. trees & shadows Macro-texture (Structure) Texture cell >> Resolution cell E.g. Field boundaries Randomly distributed Scene dependent Scene dependent ~ 40 Px ~ 100 Px ~ 500 Px 12:21:41 PM 60

61 ESA Radar image Texture Speckle, that appears as - Texture is scale grains dependent! of the same size as or larger than the resolution cell, and having a random brightness. This texture is inherent to the radar system; it does not correspond to a real variation from one resolution cell to another. Texture 12:21:41 PM 61

62 ESA Radar image Texture - Texture is scale dependent! Texture Scene texture" that is the natural variation of average radar backscatter on a scale of several resolution cells or more. Taking forests as an example, the high backscatter from the part of the tree facing the radar appears near the shadow of the opposed part of the tree away from the radar. The result is a grainy texture whose elementary unit covers several resolution cells (depending on the spatial resolution of the system). It is this component of image texture that is most useful in the interpretation of the radar image. 12:21:41 PM 62

63 ESA Radar image Texture Macro-texture corresponds to variations in radar brightness that extend over many resolution cells. It can be, for example, field boundaries, forest shadows, roads or geologic lineaments. The structure parameter is extremely important for radar imagery interpretation, especially in geology and oceanography. It is generally assessed using edge or other pattern detection techniques. - Texture is scale dependent! Texture 12:21:41 PM 63

64 ESA Radar image Texture - Texture is scale dependent! Texture 12:21:41 PM 64

65 SAR-EDU, Classification of texture measures Texture 3 rd order e.g. Variograms 2 nd order Relation between groups of pixel values Matrices of grey-level Dependence (GLDM) 1 st order Directly calcualted from the image data Variance (σ²) or Mean (µ) 12:21:41 PM 65

66 The GLCM - Grey Level Co-occurence Matrix Texture Also called Spatial Gray-level Dependence Matrix (SGDM) Tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image Calculation of the geometrical relation between 2 Pixel by: Distance Direction GLCM contains frequencies of the occurrence of neighboring pixels in a defined distance and a defined greyvalue difference in a certain direction The GLCM is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image. GLCM texture considers the relation between two pixels at a time, called the reference and the neighbour pixel. Each pixel within the window becomes the reference pixel in turn, starting in the upper left corner and proceeding to the lower right. Pixels along the right edge have no right hand neighbour, so they are not used for this count. 12:21:41 PM 66

67 SAR-EDU, Texture The GLCM - Grey Level Co-occurence Matrix Horizontal Vertical 4 4 image matrix with 4 grey values: Image Matrix d Distance r Direction Right Diagonal Left Diagonal 12:21:41 PM 67

68 (Hall-Beyer 2004) Normalization of the GLCM Matrix GLCM expressed as probability: the number of times this outcome occurs, divided by the total number of possible outcomes. Texture where i is the row number and j is the column number. 12:21:41 PM 68

69 (Hall-Beyer 2004) Classification of GLCM Texture Measures Texture GLCM Measures Contrast group Orderliness group Stats group Contrast Dissimilarity Homogeneity Angular second moment Max. probability Entropy GLCM Mean GLCM Variance GLCM Correlation 12:21:41 PM 69

70 (Hall-Beyer 2004) Emphasizing differences or similarities of grey values Contrast Group Contrast Square of GV differences Dissimilarity Absolut difference of GV Homogeneity Inverse to contrast Contrast (CON): (this is also called "sum of squares variance") Dissimilarity (DIS): In the Contrast measure, weights increase exponentially (0, 1, 4, 9, etc.) as one moves away from the diagonal. However in the dissimilarity measure weights increase linearly (0, 1, 2,3 etc.). Homogeneity (HOM) (also called the "Inverse Difference Moment") Dissimilarity and Contrast result in larger numbers for more contrasty windows. If weights decrease away from the diagonal, the result will be larger for windows with little contrast. Homogeneity weights values by the inverse of the Contrast weight, with weights decreasing exponentially away from the diagonal Contrast Dissimilarity Homogeneity Texture where i is the reference pixel and j neighbor pixel. 12:21:41 PM 70

71 (Hall-Beyer 2004) Orderliness Group How regular (orderly) are the pixels within the window? ASM Normalized Matrix weighted by itself Entropy Measure for randomness Texture Angular Second Moment Entropy where i is the reference pixel and j neighbor pixel. 12:21:41 PM 71

72 (Hall-Beyer 2004) Descriptive Statistics Group Texture Mean Calculated for i or j Variance Relies on the mean and the dispersion around the mean Correlation Measures the linear dependency of grey levels on those of neighbouring pixels GLCM Mean GLCM Variance GLCM Correlation where i is the reference pixel and j neighbor pixel. 12:21:41 PM 72

73 Forest Cover Assessment using Texture Texture 12:21:41 PM 73

74 Ralf Knuth, University of Jena ( Forest Cover Assessment using Texture Texture 12:21:41 PM 74

75 Ralf Knuth, University of Jena ( Texture w057s11 Google Earth 12:21:41 PM 75

76 Ralf Knuth, University of Jena ( Texture w057s11 TerraSAR-X Intensity HH 5 m spatial resolution Speckle filtered db :21:41 PM 76

77 Ralf Knuth, University of Jena ( Texture w057s11 RGB-Composite Dissimilarity Mean Intensity 12:21:41 PM 77

78 Ralf Knuth, University of Jena ( Texture w057s11 Classification Result MMU 0.5 ha Water Tree Cover (71-100%) Other Land (0-10%) Tree Cover Mosaic (11-70%) 12:21:41 PM 78

79 Ralf Knuth, University of Jena ( Texture w057s11 Classification Probability Forest 100 % 0 12:21:41 PM 79

80 Delineation of Built-up Area Texture using Texture 12:21:41 PM 80

81 SAR-EDU, Delineation of Built-up Area Texture a) SAR intensity image b) Speckle analysis c) Derived texture image d) Topographic map Calculation of texture image (speckle divergence) 12:21:41 PM Thomas Esch, DLR-DFD ( 81

82 Texture Built-up area SAR-EDU, Delineation of Built-up Area Mapping of urban areas from TerraSAR-X stripmap data (3m), Nairobi, Kenya 12:21:41 PM Thomas Esch, DLR-DFD ( 82

83 80 km Texture SAR-EDU, Delineation of Built-up Area Settlement mask derived from TerraSAR-X data of Shanghai area, China (40 m) 12:21:41 PM Thomas Esch, DLR-DFD ( 83

84 SAR-EDU, Delineation of Built-up Area Texture TerraSAR-X intensity Derived texture image Generated urban footprint (40 m) 12:21:41 PM Thomas Esch, DLR-DFD ( 84

85 SAR-EDU, Delineation of Built-up Area Texture Hai'an (CN) Naples (IT) Braunschweig (DE) Paramaribo (SR) Osijek (HR) 12:21:41 PM Thomas Esch, DLR-DFD ( 85

86 Thank you! & any question 12:21:41 PM 86