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- 1. Introduc)on BPT construc)on Pruning strategy Conclusions Improved BINARY PARTITION TREE construc8on for hyperspectral images: Applica8on to object detec8on 1,2 2 1 3 S.Valero , P. Salembier , J. Chanussot, C.M.Cuadras 1 GIPSA-‐Lab, Signal & Image Dept, Grenoble-‐INP, Grenoble, France 2 Signal Theory and Comunic. Dept, Technical University of Catalonia, Spain 3 University of Barcelona, Spain
- 2. Introduc)on BPT construc)on Pruning strategy Conclusions Outline 1 Introduc)on v Hyperspectral imagery v BPT 2 Binary Par))on Tree Construc)on v Region Model v Merging Criterion 3 Pruning Strategy v Road detec)on v Building detec)on 4 Conclusions
- 3. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions Outline 1 Introduc)on v Hyperspectral imagery v BPT 2 Binary Par))on Tree Construc)on v Region Model v Merging Criterion 3 Pruning Strategy v Road detec)on v Building detec)on 4 Conclusions
- 4. Introduc)on BPT construc)on Hyperspectral imagery Pruning strategy BPT Conclusions Hyperspectral Imagery v Different analysis techniques have been proposed in the literature. Most of them process the pixels individually, as an array of spectral data without any spa)al structure v Each pixel is a discrete spectrum containing the reflected solar radiance of the X Pxy N spa)al region that it 1 represents Pxy N Radiance 2 1 Wavelength Pixels are studied y as isolated discrete spectra
- 5. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions Hyperspectral Imagery v The ini)al pixel-‐based representa)on is a very low level and unstructured representa)on v Instead of working with a pixel-‐based representa)on, Binary Par88on Trees 1 are an example of a poweful structured region-‐based image representa)on. 2 v The construc)on of Binary Par88on Trees for hyperspectral images has been recently proposed 1 P. Salembier and L. Garrido. Binary par**on tree as an efficient representa*on for image processing, segmenta*on, and informa*on retrieval. IEEE Transac)ons on Image Processing, vol.9(4), pp.561-‐576, 2000. 2 S.Valero, P.Salembier and J.Chanussot. New hyperspectral data representa)on using Binary Par))on Tree. IEEE Proceedings of IGARSS, 2010
- 6. Introduc)on BPT construc)on Hyperspectral imagery Pruning strategy BPT Conclusions BPT v BPTs can be interpreted as a structured image representa)on containing a set of hierarchical regions stored in a tree structure v Each node represen)ng a region in the image, BPTs allow us to extract many par))ons at different levels of resolu)on
- 7. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions BPT v BPTs can be interpreted as a structured image representa)on containing a set of hierarchical regions stored in a tree structure v Each node represen)ng a region in the image, BPTs allow us to extract many par))ons at different levels of resolu)on
- 8. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions BPT v BPTs can be interpreted as a structured image representa)on containing a set of hierarchical regions stored in a tree structure v Each node represen)ng a region in the image, BPTs allow us to extract many par))ons at different levels of resolu)on ? How can BPT be extended to the case of hyperspectral data ?
- 9. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions Aim: BPT for HS image analysis Hyperspectral Classifica)on image CONSTRUCTION PRUNING Object detec)on Segmenta)on v We propose to construct a BPT in order to represent an HS image with a new region-‐based hierarchical representa)on Hyperspectral BPT image representa)on
- 10. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions Aim: BPT for HS image analysis Hyperspectral Classifica)on image CONSTRUCTION PRUNING Object detec)on Segmenta)on v In this paper: Pruning strategy aiming at object detec)on is proposed BPT Hyperspectral Search image The pruning look for regions characterized by some features
- 11. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Outline 1 Introduc)on v Hyperspectral imagery v BPT 2 Binary Par))on Tree Construc)on v Region Model v Merging Criterion 3 Pruning Strategy v Road detec)on v Building detec)on 4 Conclusions
- 12. Introduc8on BPT construc8on Pruning strategy Conclusions Hyperspectral Imagery v The BPT is a hierarchical tree G structure represen)ng an image G v The tree leaves correspond to individual pixels, whereas the root E represents the en)re image F F v The remaining nodes represent regions formed by the merging of E two children E C D C D v The tree construc)on is performed by an itera)ve region merging algorithm A B B A B C D
- 13. Introduc8on BPT construc8on Pruning strategy Conclusions Hyperspectral Imagery v The BPT is a hierarchical tree structure represen)ng an image v The tree leaves correspond to individual pixels, whereas the root represents the en)re image v The remaining nodes represent regions formed by the merging of two children C D v The tree construc)on is performed by an itera)ve region merging algorithm A B B A B C D
- 14. Introduc8on BPT construc8on Pruning strategy Conclusions Hyperspectral Imagery v The BPT is a hierarchical tree structure represen)ng an image v The tree leaves correspond to individual pixels, whereas the root represents the en)re image v The remaining nodes represent regions formed by the merging of E two children E C D C D v The tree construc)on is performed by an itera)ve region merging algorithm A B B A B C D
- 15. Introduc8on BPT construc8on Pruning strategy Conclusions Hyperspectral Imagery v The BPT is a hierarchical tree structure represen)ng an image v The tree leaves correspond to individual pixels, whereas the root E represents the en)re image F F v The remaining nodes represent regions formed by the merging of E two children E C D C D v The tree construc)on is performed by an itera)ve region merging algorithm A B B A B C D
- 16. Introduc8on BPT construc8on Pruning strategy Conclusions Hyperspectral Imagery v The BPT is a hierarchical tree G structure represen)ng an image G v The tree leaves correspond to individual pixels, whereas the root E represents the en)re image F F v The remaining nodes represent regions formed by the merging of E two children E C D C D v The tree construc)on is performed by an itera)ve region merging algorithm A B B A B C D
- 17. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Hyperspectral Imagery The crea)on of BPT implies two important no)ons Rij v Region model MRi It specifies how an hyperspectral region O(Ri,Rj) is represented and how to model the O(Ri,Rj) union of two regions. Rj Ri v Merging criterion O(Ri,Rj) The similarity between neighboring regions determining the merging order
- 18. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Aim: BPT for HS image analysis Hyperspectral Classifica)on image CONSTRUCTION PRUNING Object detec)on Segmenta)on v How to represent hyperspectral image regions? v Which similarity measure defines a good merging order?
- 19. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Region Model We propose a non-‐parametric sta)s)cal region model 2 Radiance Pixel 1 consis)ng in a set of N probability density Pixel 2 func)ons Pixel 3 A B where each Pi represents the probabili)y that the spectra data Wavelength λ set has a specific radiance value λi in the wavelength λi Hλi 2 F. Calderero and F. Marques.Region-‐Merging techniques using informa*on theory sta*s*cal measures. IEEE Transac)ons on Image Processing, vol.19, pp.1567-‐1586, 2010. B A Nbins
- 20. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Aim: BPT for HS image analysis Hyperspectral Classifica)on image CONSTRUCTION PRUNING Object detec)on Segmenta)on v How to represent hyperspectral image regions? v Which similarity measure defines a good merging order?
- 21. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Merging Criterion Step 1 Step 2 Principal Coordinates Mul8dimensional Scaling Associa8on Measure Mul8dimensional Scaling Principal Coordinates
- 22. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Merging Criterion Step 1 Mul8dimensional Principal Coordinates Scaling Analyze the inter-‐waveband similarity rela)onships for each data via metric scaling to obtain the principal coordinates Mul8dimensional Scaling Principal Coordinates
- 23. Introduc8on BPT construc8on Region Model Pruning strategy Merging Criterion Conclusions Merging Criterion: Step 2 Step 2 Principal Coordinates PC1 An associa)on measure Mul8dimensional is defined by considering Scaling that PC1 and PC2 form a Associa8on mul)variate regression Measure model Mul8dimensional Scaling Principal Coordinates PC2 PC1= PC2 β +e Are Regression Coefficients equal to 0 ???
- 24. Introduc8on BPT construc8on Pruning strategy Conclusions Rosis Hyperspectral data Data Set : Rosis Pavia Center 103 bands RGB Composi)on 103 bands BPT is constructed by using the proposed merging order Nregions=22 Nregions=32 Nregions=42
- 25. Introduc8on BPT construc8on Pruning strategy Conclusions Rosis Hyperspectral data Ground truth manually created A symmetric distance for object evalua)on: It is defined as the he minimum number of pixels whose labels should be changed to achieve perfect matching, normalized by the total number of pixels of the image minus one NRegions=22 NRegions=22 Symmetric distance Symmetric distance to ground truth to ground truth equal to 0.48 equal to 0.227 [Tilton, 2005] RHSEG soiware Hierarchical BPT level
- 26. Introduc8on BPT construc8on Road detec)on Pruning strategy Building detec)on Conclusions Outline 1 Introduc)on v Hyperspectral imagery v BPT 2 Binary Par))on Tree Construc)on v Region Model v Merging Criterion 3 Pruning Strategy v Road detec)on v Building detec)on 4 Conclusions
- 27. Introduc8on BPT construc8on Hyperspectral imagery Pruning strategy BPT Conclusions Aim: BPT for HS image analysis Hyperspectral Classifica)on image CONSTRUCTION PRUNING Object detec)on Segmenta)on v Pruning strategy aiming at object detec)on is proposed BPT Hyperspectral Search image The pruning look for regions characterized by some features
- 28. Introduc8on BPT construc8on Road detec)on Pruning strategy Building detec)on Conclusions Object Detec8on Strategy v Hyperspectral object detec)on has been mainly developed in the context of pixel-‐wise spectral classifica)on. v Objets are not only characterized by their spectral signature. v Spa)al features such as shape, area, orienta)on can also contribute significantly to the detec)on. v Roads appear as elongated structures having fairly homogeneous radiance values usually corresponding to asphalt. Besides the informa8on provided by asphalt spectrum, a road contains important spa8al features
- 29. scheme. The strategy is to analyze the BPT using a set of descrip- with the classical RHSEG [11]. In the case of R scheme. The strategy is to analyze the BPT using a set of descrip- ity criterion used is SAM with spectral clustering w Introduc8on forfor each node. The work presented here proposes the tors computed tors computed each node. The work presented here proposes the ity criterion used is SAM with spectral clustering evaluate the resulting partitions, the symmetric di analysis of three different descriptors for each node: evaluate the resulting partitions, the symmetric BPT construc8on different descriptors for each node: analysis of three Road detec)on is used as a partition quality evaluation. Having a is used as a partition quality evaluation. Having Pruning strategy D = {Dshape , Dspectral , Darea } ground truth GT , the symmetric distance corresp Building detec)on D = {Dshape , Dspectral , Darea } (7) (7) ground truth GT , the symmetric distance corre mum number of pixels whose labels should be cha Conclusions mum number of pixels whose labels should be The proposed shape, spectral and area descriptors are related P to achieve a perfect matching with GT , norma The proposed shape, spectral and area descriptors are related P to achieve a perfect matching with GT , nor to the specficic object of interest. Studying D from the leaves to number of pixels in the image. The manually crea to the specficic object of interest. Studying D from the leaves to number of pixels in the image. The manually c Object Detec8on Strategy the root, the approach consists in removing all nodes that signifi- in Fig. 4(b). the root, the approach consists in removing all nodes that signifi- in Fig. 4(b). cantly differ from the characterization proposed by a reference Dref . Fig. 4(c)(d) show the segmentation results obtain cantly differ from the characterization proposed by a reference Dref . Fig. 4(c)(d) show the segmentation results obta Hence, given this reference, the idea consists in considering that the RHSEG, respectively. In both cases, the resulting Hence, given this reference, the idea consists in considering that the RHSEG, respectively. In both cases, the resultin searched object instances are defined by the closest nodes to the root 63 regions. Comparing both results, the quantiti searched object instances are defined by the closest nodes to the root 63 regions. Comparing both results, the quan node that have descriptors close to the Dref . model. In order to visual evaluation corroborates that the partition ex node that have descriptors close to the Dref . model. In order to visual evaluation corroborates that the partition illustrate the generality of the approach, we describe two detection BPT are much closer to the ground truth than the on illustrate the generality of the approach, we describe two detection BPT are much closer to the ground truth than the examples: roads and building in urban scenes. RHSEG. This experiment has been done with sev examples: roads and building in urban scenes. RHSEG. This experiment has been done with s acquired by different hyperspectral sensors, but, d acquired by different hyperspectral sensors, but tations, we can only present one data set. Howeve 3.1. Detection of roads 3.1. Detection of roads We analyse each tations, we can only present one data set. Howe were the same on the remaining dataset. were the same on the remaining dataset. Roads appear as elongated structures having fairly homogeneous ra- A second set of experiments are conducted no Roads appear as elongated structures having fairly homogeneous ra- diance values usually corresponding to asphalt. Given their charac- BPT to look for the A second set of experiments are conducted tection and recognition. We compare the classica v Firstly, the spa)al and the spectral diance values usually corresponding to asphalt. Given their charac- teristic shape, Dshape is the elongation of the region. In order to tection and recognition. We compare the class sification against the strategy proposed in section 3 compute it, we first define the smallest rectangular bounding box descriptors are computed for all compute it, we first define the smallest rectangular bounding box nodes having teristic shape, Dshape is the elongation of the region. In order to sification against the strategy proposed in sectio road detection. The pixel-wise classification con road detection. The pixel-wise classification c specific spa8al and BPT nodes spectral descriptors v Secondly, following a bojom-‐up strategy, the pruning select nodes closer to the root which have a low elonga)on, a high correla)on between asphalt and an area higher than a threshold Star8ng from the leaves
- 30. Introduc8on BPT construc8on Road detec8on Pruning strategy Building detec)on Conclusions Object Detec8on: Example of Roads v Roads appear as The ra)o between the height and the width of the minimum elongated structures bounding box containing the region Oriented Bounding Box containing the region height width
- 31. Introduc8on BPT construc8on Road detec8on Pruning strategy Building detec)on Conclusions Object Detec8on: Example of Roads v Roads have fairly Correla)on coefficient between the mean spectra of the homogeneous radiance region and the asphalt reference spectrum values usually corresponding to asphalt Mean Correla)on Spectrum Coefficient Pixel 1 Asphalt reference spectrum Radiance Pixel 2 Pixel 3 Wavelength λ
- 32. Introduc8on BPT construc8on Road detec)on Pruning strategy Building detec)on Conclusions Object Detec8on: Example of Roads Par88ons contained in BPT Hydice Hyperspectral image 27 regions 37 regions 57 regions Pixel-‐wise Asphalt detec)on BPT pruning strategy (Spectra whose Correla)on oriented to object with asphalt is higher than 0.9) detec)on
- 33. Introduc8on BPT construc8on Road detec)on Pruning strategy Building detec)on Conclusions Object Detec8on: Example of Building Par88ons contained in BPT Hydice Hyperspectral image 27 regions 37 regions 57 regions Pixel-‐wise Building detec)on BPT pruning strategy (Spectra whose Correla)on with oriented to object reference is higher than 0.9) detec)on
- 34. Introduc)on BPT construc)on Pruning strategy Conclusions Conclusions v A new region merging algorithm has been presented here to construct BPTs as a hyperspectral region-‐based and hierarchical representa)on. v Being a generic representa)on, many tree processing techniques can be formulated as pruning strategies for many applica)ons. Here, as an example of BPT processing, a pruning strategy has been proposed for object detec)on. v A new similarity measure for merging hyperspectral regions have been proposed taking into account spa)al and spectral correla)ons. It introduces a local dimension reduc)on which is different from the classical point of view where the reduc)on is applied globally on the en)re image. v The processing example has shown the advantage of using region-‐based representa)ons.