Object counting in high resolution remote sensing images with OTB

Motivation Original Simpliﬁed Conclusion Object counting in high resolution remote sensing images with OTB E. Christophe1 , J. Inglada2 1 C ENTRE FOR R EMOTE I MAGING , S ENSING AND P ROCESSING , N ATIONAL U NIVERSITY OF S INGAPORE 2 C ENTRE N ATIONAL D ’É TUDES S PATIALES , TOULOUSE , F RANCE IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Outline Motivation Original solution Workflow Pan Sharpening Classification Segmentation: Mean shift Vector data Simplified versions Trade-off Description Results Conclusion IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Motivation Object counting Correspond to a wide range of problems for remote sensing data users Often have to be performed on large area Time consuming Examples Houses in a city: particularly for country where urban planning data is not available Tents in refugee camp Tree stands in a ﬁeld IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion PRRS 2008 algorithm performance contest Time constraints: can’t spend months reﬁning the algorithm Deliver a result: whole processing chain required Each step can be improved Goal: illustrate the on the shelf approach Contest Count the building from a Quickbird scene over Legaspi, Philippines XS and Pan images were provided Aksoy et al., ”Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 algorithm performance contest,” in 5th IAPR Workshop on Pattern Recognition in Remote Sensing, Tampa, Florida, Dec. 2008. IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Outline Motivation Original solution Workflow Pan Sharpening Classification Segmentation: Mean shift Vector data Simplified versions Trade-off Description Results Conclusion IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Algorithm description Pan Mul IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Algorithm description Pan Pan sharpening Mul IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Mul IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Segmentation Mul IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Segmentation Vectorization Mul IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Segmentation Vectorization Mul Edge dectect. IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Segmentation Vectorization Refinement Mul Edge dectect. IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Algorithm description Classification Pan Pan sharpening Segmentation Vectorization Refinement Obj Mul Edge dectect. IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Preprocessing Pansharpening The pansharpening is the first step to perform to take advantage of the high resolution of the Panchromatic band (61 cm) with the four spectral bands of the multispectral. Pan Mul Pan Shapening IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Classification Obvious sources of errors There is some obvious sources of error: boat in the middle of the water which look like houses, cars in the middle of the street Classification (used as a mask) can help remove these sources of error Classification Here we used a simple classification by SVM Non classification specialists just provided a few samples per class (water, vegetation, road, shadows, 4 colors of buildings) Only a pixel classification: no use of texture here (that was before all the textures were introduces in OTB) IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data QB scene Land cover classification IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Segmentation: Mean shift Too much details Higher resolution is better but. . . sometimes, you would like less details (roof superstructures, cars) What details to remove? Mean shift algorithm D. Commaniciu,“Mean shift: A robust approach toward feature space analysis,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, May 2002. IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Pan Sharpened Mean shift clustering IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Refining the boundaries Simplification Easier to handle vector data than raster: vectorization The vectorization led to too many contour point: simplification of the points which are roughly aligned Fine adjustment Using an image contour as an input, an energy is computed along the polygon contour Introducing a random perturbation in the position of each point, the energy is maximized Only a very basic optimization used here (ground for improvement) IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Filtering Filtering on compacity Buildings are usually compact A C = 4π L2 IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Workflow PanSharp Classif. Segment. Vector data Performances on the contest Results Two results were submitted with a difference mainly in the classification One result was very close with 3600 building detected for 3065 in the ground truth Interesting to see that most other algorithms tend to underdetection while the proposed algorithm tends to overdetect. Evaluation criteria {Correct, over, under, missed} detection and false alarm rates based on Overlapping Area Matrix Maximum-weight bipartite graph matching Normalized Hamming distance Clustering indices (Fowlkes-Mallows and Jaccard index) IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Workﬂow PanSharp Classif. Segment. Vector data Performances on the contest Conclusion on these results Particularly hard to conclude given the wide variety of criteria: organizer of the contest have been careful not to declare an overall winner However, the proposed methods provided good performances (particularly on the clusterings indices criteria) with a bias towards over segmentation. IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Trade-off Description Results Outline Motivation Original solution Workflow Pan Sharpening Classification Segmentation: Mean shift Vector data Simplified versions Trade-off Description Results Conclusion IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Trade-off Description Results Trade-off of the previous method Drawbacks The previous method relies on the classification of the image require a good understanding of the algorithm that follow influence significantly the output Simplified version different trade-offs on complexity-performance remove the classification step just require the operator to click on several (2 to 5) examples of objects IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Trade-off Description Results Simpliﬁed version Algorithm Produce a likelihood map of the region containing the objects of interest Followed by the same step as the previous algorithm: segmentation, vectorization,. . . Likelihood map: 2 choices Spectral angle One class SVM IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Trade-off Description Results Algorithm description One class SVM Spectral angle Pan Pan sharpening Segmentation Vectorization Reﬁnement Obj Mul Edge dectect. IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Trade-off Description Results Results of the simpliﬁed version Poorer preformances Not as good as the original one (expected) ⇒ required to understand the algorithm Advice Spectral angle: spectral characteristics of the objects are stable SVM: better when object have radiometric differences but more samples are required IGARSS 2009, Cape Town

Motivation Original Simplified Conclusion Outline Motivation Original solution Workflow Pan Sharpening Classification Segmentation: Mean shift Vector data Simplified versions Trade-off Description Results Conclusion IGARSS 2009, Cape Town

Motivation Original Simpliﬁed Conclusion Conclusion Modular processing chain An application with GUI is available It can be used for processing remote sensing images (no constraint on the size) Can be easily modiﬁed and improved as the steps are modular and follow the pipeline philosophy. IGARSS 2009, Cape Town

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Object counting in high resolution remote sensing images with OTB

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