Automatic extraction of forests from historical maps based on unsupervised classification in the CIELab color space

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Claribel Merritt
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1 Automatic extraction of forests from historical maps based on unsupervised classification in the CIELab color space P.-A. Herrault 1,2, D. Sheeren 1, M. Fauvel 1, M. Paegelow 2 1 DYNAFOR Lab. UMR 1201 INP-ENSAT / INRA University of Toulouse 2 GEODE Lab. UMR 5602 UTM / CNRS University of Toulouse

2 Olds maps contain specific information (historical places, historical land cover, building footprints) Interesting for various studies about long-term changes of landscapes,urban development or coastlines evolution For few years, a lot of maps are available thanks to National Archives

3 Traditional approach to capture objects in historical maps are based on user intervention (for digitizing) As well-known, very time-consuming, very subjective and not reproducible on large areas Need to develop automatic approaches

4 Some problems to capture automatically features on historical maps Overlapping of planimetric elements Poor quality because of scanning procedure Maps without any colors

5 Several authors have already proposed automatic methods to capture geographical objects (Ansoult et al.1990; Li et al.1999; Leyk 2006) Two major steps used in automatic extraction on scanned thematic maps Features extraction(classification/segmentation) Clean-up process (Before or/and after extraction process)

6 Historical maps contain different kinds of «Noise» overlapping of planimetric elements Shading effect due to scanning procedure Method based on image-filtering techniques Convolution filters Morphological filters

7 Assigning a new value in each pixel using the pixel values in its neighborhood thanks to a mobile window for convolution thanks to a structuring element for morphological Ex : Median Filtering 3, 3, 3, 4, 4, 5, 5, 5, 10

8 Lines (Kaneko,1992; Mariani and al.1997) Data capture on historical maps may concern various types of features Text (Cao and al.2002; Centeno 1998) Regions (Shaw and al.2011; Chiang 2009) Symbols (Gamba and Mecocci 1999; Boesch 1996)

9 Developing automatic procedure to extract forests features from the historical Map of France (19th century) Today, no automatic method available for this map

10 Developing automatic procedure to extract forests features from the historical Map of France (19th century) Reproducible on large areas User intervention limited Sufficiently generic in order to test it on other objects or other raster-color maps

12 Three excerpts of 1500*1500 pixels each Differences in terms of quality, slope and relief Differences of colors for the forest features

15 Original map(s) Dilatation Median filtering Low-pass filtering Filtered map(s)

16 Original map(s) Dilatation Median filtering Low-pass filtering Filtered map(s) Dilatation (square of 5*5 pixels) filling all possible holes within forests created by text, symbols, elevation contour lines

17 Original map(s) Dilatation Median filtering Low-pass filtering Filtered map(s) Median filter (window of 5*5 pixels) reducing remaining elevation contour lines while preserving edges and colors

18 Original map(s) Dilatation Median filtering Low-pass filtering Filtered map(s) Low-pass filter (window of 5*5 pixels) Removing the backround noise without blurring image

20 Why? RGB (Red Green Blue) not always suited to perform automatic extraction non-uniformity of the luminosity lack of human perception (Angulo and Serra 2003) Other color-space well-known for graphic applications (HSV,HLS) but less suitable for image-processing

21 Specificities? an axis L (Luminosity) perpendicular to ab planes one ab plane for each value of Luminosity

22 Specificities? an axis L (Luminosity) perpendicular to ab planes one ab plane for each Value of Luminance Advantages? possibility to consider each variation of one color like a succession of pure colors increasing uniformity of the image

24 +b Centroids -b -a +a Données non-classées

25 Centroids Example for K=3 +b +b -b -a +a Data not classified -b -a +a Data classified

27 Correcting the non-inclusion of some elements upstream the treatments

28 Correcting the non-inclusion of some elements upstream the treatments Morphological opening removing small isolated pixels which are non-forest features Contextual rules filling holes within forest features after classification

30 Original Map Dilatation Median Filter Filtered Map Low-pass filter

31 Carte originale Dilatation Filtre médian Carte filtrée Map Original Map Filtre passe-bas Filtered Map

32 Classification in RGB color space Filtered Map Classification in L*a*b color space

33 Classification in RGB color space Filtered Map Classification in L*a*b color space

34 Original Map Pre-processings+classification Extracted features not corrected

35 Original Map Pre-processings+classification Extracted features not corrected Post-processings Final extracted forest Reconstruction Extracted features corrected (binary)

36 Excerpt 1 Excerpt 2 Validation by comparing with manual extraction High Global Accuracy Trend to under-detect forests features Excerpt 3 Original Map Binary extraction layer Final extraction layer

37 Excerpt 1 Excerpt 2 Validation by comparing with manual extraction High Global Accuracy Trend to under-detect forests features Excerpt 3 Original Map Binary extraction layer Final extraction layer

under-detect forests features Excerpt 3 Original

38 Excerpt 1 Excerpt 2 Validation by comparing with manual extraction High Global Accuracy Trend to under-detect forests features Excerpt 3 Original Map Binary extraction layer Final extraction layer

39 Method relatively robust L*a*b color space well suited to low-quality maps Trend to under-detect forest features ************************************************************************ Improving post-processings steps (contextual rules) Testing the method on other objects or others rastercolor maps

40 Thank you for your attention

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space P.-A Herrault, D Sheeren, M Fauvel, M Paegelow To cite this version: P.-A Herrault, D