Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS

1. Remote sensing and modeling in forestry Lecture 12 Classification 2 Artificial Neural Networks Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS

2. Classification methods Generally are divided in two main groups Unsupervised: based only on spectral characteristics, without any use of ground based data Supervised: use known examples collected at the ground to parameterize the system

3. Supervised classification The first step in a supervised classification is to identify examples of the classes of interest in the image (training sites). The training sites are areas as much as possible homogeneous that have to represent all the existing classes For each class there should be at least N x 10 pixels where N is number of spectral bands used for the classification.

4. Minimum distance method with Max Dist Band 2 1 = NC 1 2 = Crop Urban 3 = NC Sand 2 Crop Grass Forest 3 Water Band 1

5. Also here we can specify a max limit 1 = Urban 2 = Crop 3 = NC Maximum Likelihood method Band 2 1 = Urban 1 2 = Crop Urban 3 = Urban Sand 2 Crop Grass Forest 3 Water Band 1

6. Input dataset Input layer Hidden layer Output layer = unit Output = connection Artificial Neural Networks - ANNs What is an ANN It is a network of many simple processors ("units"), each possibly having a small amount of local memory. The units are connected by communication channels ("connections") which usually carry numeric (as opposed to symbolic) data, encoded by any of various means. The units operate only on their local data and on the inputs they receive via the connections.

7. 1 n1 x1 b w1 w1 x1 Σ x2 n2 w2 x2 y a g w2 wn xn wn xn nn The unit or node ni = previous layer nodes xi = input to the unit wi = connection weigh g = transfer function y = output from the unit

8. Multilayer Feed-forward Back propagation ANN (BPN) BPN are a class of ANN with these characteristics: • They are able to model also complex relationships • The ANN training is supervised • The nodes are organized in layers • The information flow is unidirectional from the input layer to the output layer • There are not connections between nodes of the same layer • The error in the output estimate is retro-propagated from the output layer to the input layer

9. To avoid overfitting, the dataset used in the ANN training is split in 3 subsets: • Training set • Test set • Validation set Error p Epochs BPN: how it works The ANN is trained using a dataset of examples with input and correct output. For each example the ANN-output is compared with the correct-output and the error is minimized modifying the connection weighs Once the ANN has been trained, the connections’ weighs are fixed and it is possible to use the Network with only the input data

10. Ekj b wkj a How to back propagate the error? There are different training algorithms available. The simplest but also most used is based on the gradient descent technique. Considering the connection between node k and node j :

11. ANN and classification ANNs used for classification have an output node for each of the classes and the result will be a series of images (one for each node). For this reason the ANNs can be considered also a soft classifier.

12. Training set Used to evaluate the errors Used to assess the connection weights Test set Validation set Input dataset Input layer Hidden layer Output layer = node or unit Output = connection Artificial Neural Networks training Input and Output database Used to evaluate the trained ANN Trained ANN

13. REMOTE SENSING OTHERS SPATIAL DATA Bands, FAPAR, VI Land Cover, Soil map, Climate, … OUTPUT DATA FROM EDDY COVARIANCE Meteo and fluxes NEP ANNs for carbon fluxes upscaling Papale & Valentini- GCB 2003

14. Unsupervised classification It is not needed to have ground data to train the algorithm The classification will cluster pixels on the basis of the radiance values only putting together the one that are more similar. Often it is possible to set un a number of parameters like the maximum number of clusters of the minimum number of pixels to create a new cluster

15. Unsupervised classification

16. Classification accuracy and validation One method is the use of the confusion matrix where classification and ground truth are compared for each pixel. There are two possible errors: Omission errors, when a pixel of a certain class X is not classified as X Commission errors, when a pixel is classified as part of class X but in reality it is not member of that class

17. Classification accuracy and validation GROUND TRUTH COMMISSION ERROR 18 out of 43 pixels have been erroneously classified as A A class is mainly confounded with class C CLASSIFICATION

18. Classification accuracy and validation GROUND TRUTH OMISSION ERROR 7 pixels out of 32 have been omitted from class A CLASSIFICATION

19. GENERAL ACCURACY It is the ratio between the sum of the pixels correctly classified and the total number of pixels used in the validation Classification accuracy and validation GROUND TRUTH CLASSIFICATION

20. USER ACCURACY It is the ratio between the sum of the pixels correctly classified in each single class and the total number of pixels classified in the same class The user that will visit a pixel classified as class A has 58.1% possibilities that the area is in reality a part of class A Classification accuracy and validation GROUND TRUTH CLASSIFICATION

21. PRODUCER ACCURACY It is the ratio between the sum of the pixels correctly classified in each single class and the total number of pixels that I should have classified in that class The producer knows that he classified 78.1% of the pixel of class A as part of this class and the rest have been erroneously classified in others classes Classification accuracy and validation GROUND TRUTH CLASSIFICATION

22. What can be included in a mix pixel? Bare Soil Tree River Tree shadow Grass

23. Pixel Unmixing Can we identify what is included in a mix pixel? Yes with the pixel unmixing Starting from pure surfaces of the classes of interest (endmembers), we can estimate how much of each class is included in each pixel

24. Linear Spectral Unmixing Basic assumption: reflectance of the mixed pixel is a weighted linear combination (for the extension) of the reflectance of the end members Fi= percentage of the endmember i in the pixel DN= reflectance of the pixel in the  spectral band DN,i= reflectance of the endmember i in the  spectral band E=error

25. Linear Spectral Unmixing There will be one of this equation for each pixel where the DNs will be different but the Fx are the same (percentage of the endmember in the pixel) • DN is from the image, DN,I can be measured in the field or with pure pixels. If I < number of bands + 1, we can calculate Fi andE

26. Segmentation and classification object-oriented • Segmentation methods are used to identify in the image polygons characterized by a variability inside the polygon lower than the variability between polygons • Heterogeneity and variability are evaluated on the basis of the spectral characteristics and also the polygon shape • It is possible to apply the segmentation using different levels of detail and creating an hierarchical system of polygons (multi-level segmentation) • The classification object oriented is based on the polygons created with the segmentation and not the single pixel (pixel oriented)

27. Object segmentation

28. Classification 1 - spectral: based on DNs of the pixels in each polygon (mean, STD, etc); 2 – geometric: area, perimeter, shape, position etc. of the polygons. 3 – texture: structures inside the polygons (e.g. from an additional level of segmentation). 4 – hierarchical: links to others larger, smaller or neighbor polygons. 5 – thematic attributes: derived from others dataset such a DEM, a climate map etc.