Closed contour extraction application to meteorological pictures

0031 3203/82/030145 08 $03.00/0 Pergamon Press Ltd. © 1982 Pattern Recognition Society

Pattern Rt'coclnition Vol. 15, No. 3, pp. 145 152, 1982. Printed in Great Britain.

CLOSED CONTOUR EXTRACTION APPLICATION TO METEOROLOGICAL PICTURES V. LATTUAT!and D. LEMOINE Laboratoire d'Automatique du C.N.A.M., 21 rue Pinel Paris 13¢, France

(Received 19 November 1980; in revisedform 19 May 1981) Abstract--This paper deals with the problem of closed contour extraction for noisy images whose

boundaries are very fuzzy. The method used relies on simple arguments: reduction by adaptive quantization, filtering of the reduced-levels image contours and spots detection. Application of this method to meteorological satellite images proved to be successful while classical methods would have failed. Meteorological satellite Cloud cover Contour extraction Context Blurred image

INTRODUCTION Geostationary meteorological satellites provide images of the cloud cover of the Earth. These images, formated 3000 x 3000 points, are taken in infrared and visible channels, and quantized on 8 and 6 bits, respectively. Manual exploitation of the meteorological data contained in these images is a long and tedious task. With the help of the meteorologists from the C.M.S.* (France), a catalogue of the different cloud configurations interesting for weather forecasting was established. The C.M.S. gave us the task of elaborating criteria that would make it possible to distinguish each family of clouds in order to develop an automatic exploration of the images received in a numerical form. A file containing approximately 200 double images (256 x 240 points for 1000 x 1000km) and grouping together the different characteristic cloud configurations enabled us to guide our research. It very rapidly became obvious that an important criterion of classification was the shape and size of cloudy patches discernable on visible and infrared photos. But clouds have very blurred boundaries and, whereas the human eye can easily interpret the edge and contour of a cloud configuration, it is difficult for a computer to construct the closed contour of that cloud configuration. The method exposed in the following article makes it possible to obtain these closed contours. It is based on the following idea: it is easy to follow a contour when a picture presents few gray levels. So, the difficulty is starting from the initial picture to obtain a two or three level picture that will retain all contour data through the simplest possible processing. The thresholding of the initial picture does not give good results.

*C.M.S.: Centre de M6t6orologie Spatiale de Lannion.

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Starting from the initial picture and its differentiated picture, the three valued picture, on which the closed contours of the clouds will appear, will be elaborated. This method offers the advantage of associating edgedetection by gradient operator with the data contained in the pictures context. CONSTRUCTIONOF THE THREE VALUEDPICTURE The pictures contained in the file are reduced to half their size (120 x 128 points) and each point retains a gray level quantized to 8 bits.

Notions of plateau, peak and valley These notions are necessary to an adaptive quantization of the picture. The study of representations with respect to the pictures contained in the file (cf. Fig. 7) shows that it is interesting to use the notion of relief in order to define three areas in the pictures: fiat, high altitude areas that we will call Plateaux; fiat, low altitude areas that we will call valleys and sharply undulating areas that will we will call peaks. Mere thresholding makes it impossible to correctly obtain these areas that correspond to cloudy areas, the sea and cloud fringes respectively. By introducing the value of the digital gradient calculated at each point of the picture, it is now possible to characterize the three areas and assign to them the following values: level 100, peak area (high level points in the gradient picture); level 0, valley areas (low level points in the gradient and in the initial pictures) and level 200, plateau area (low level points in the gradient picture and high level points in the initial picture). The spatial gradient used is Sobel's, which has the advantage of not distorting directions. However, it thickens boundaries, which is of no inconvenience here. The thresholds that determine the high and low levels are constructed from the histograms of all the pictures filed. They are fairly insensitive parameters regarding the shape of the contours.

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Filtering of the three level picture The above three level picture has to be filtered (cf. Fig. 8). In order to obtain homogeneous and significant patches of points at 0 or 200 levels, it is necessary to eliminate the noise produced by the low gradient points (0 and 200 levels) scattered among the high level points (100 level). Similarly, it is necessary to make the peak areas homogeneous, and to suppress the black (0) and white (200) points isolated and scattered in the wide areas of gray points (100). (The filtering must be simple and quick. Convolution, DFT techniques, have thus to be dismissed, and replaced by methods based on logical tests.) This method, well-known for two level pictures, consists of two successive phases. (1) Shrinking transforms into a 100 level point any 0 or 200 point if, among the 8 neighbour points, there exists at least one point of different level. The areas containing fewer than 9 points disappear, and the size of the main areas is shrunk. (2) Re-expanding the remaining 0 and 200 level areas, by surrounding each 0 or 200 level point with a neighbourhood of 8 points of identical level. These two operations are illustrated below with a two valued

picture. Small areas disappear, whereas important areas recover their initial size. This filtering process is very rapid because it uses only logical tests. The picture

obtained after this processing is called three level filtered picture, or filtered picture. It offers homogeneous, compact and distinct areas, composed of 0, 100 and 200 level points that perfectly correspond to the areas seen on the photos. This picture is now ready for the detection of the closed contours of the areas. (cf. Fig. 9). After the differentation operation, the picture has lost two rows and two columns. So, before beginning filtering, the picture is surrounded by a line of high gradient points (100 level), which has two useful consequences: creation of a neighbourhood for the points on the boundaries of the picture, so a possible filtering of these points, and easy search for the contours, for there are no flat areas (0 or 200 level) reaching the limits of the picture.

Detection of contours and location of the areas On this pre-processed picture, the contours are obtained by following the boundaries separating the sets of 0/100 level points or the sets of 100/200 level points. Each area of the picture is processed in three phases: locating an area by pointing out the first contour point ; searching for and drawing the contour of the area ; and measuring its characteristics and erasing the area. Processing consists of scanning the picture row

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The plotted area appears as an area of points surrounded by 3 level points. In order to neutralize the first area, all its contour and inside points are forced to an arbitrary value, e. g. N = 4. This filling is done row Oo by row, in the following way: on one row, we search for contour points (3 gray level). Starting from the first point located, we force all the points of the row to gray OG OF OE level N if at least one of the two immediately following contour points is at 200 gray level. Filling goes on until Fig. 5. Succession of the picture scanning. another contour point is found on the same row. This operation can be repeated several times on each row, according to the complexity of the shape of the by row, starting from the first, until a 200 level point is processed area. found (plateau). This point, noted PTI, is the first The properties of filtering make a non-ambiguous contour point of the located area, and it is memorised drawing of the contours possible, 0 or 200 level points during the whole processing of the complete area. being gathered into two homogeneous areas, sepaStarting from the PTI point, the search for another rated by 100 level points. There can exist, inside the contour point is achieved by examination of the contours, some 100 level points that in no way prevent neigbbouring points, by a direct rotation whose center the filling of the area and can easily be erased if desired. is PTI and origin A. Rotation stops at the first 200 level Together with the filling of the area, its size is point detected (cf. point B), which becomes, in turn, a calculated by counting the number of its points. When current contour point and the new rotation center, the first area is located and erased, scanning starts while (PT1, B) becomes a contour segment. again from the point (row scanning) nearest to the The following rotation, with B as center, begins at preceding PT1 point. The next areas are filled with 5, 6, point C, so that segment BC should make a 90 ° angle 7, etc. level points and histograms of the size of the with the already known contour segment (PTI, B), and areas are simultaneously calculated. The same prosequentially examines points C ..... G and so on. cedure is applied to the search for 0 level points areas The first area is located when the departure point and makes it possible to draw the contours of the PT1 is found again. The contour points, which have valleys (cf. Fig. 10). been memorised are restricted to 3 quantized gray This method has oeen applied to several dozen levels. Before resuming scanning to find the beginning meteorological pictures and has given excellent results. of another area, the preceding area must be erased in Figures 7-11 show the successive stages of the processorder to suppress any 200 level point inside this area: ing (photographic restitution, perspective picture, such a noise would be the starting-point of a wrong three valued primitive picture, filtered picture, picture of plateaus and valleys), and the results obtained : the contour drawing of the closed contours is significant and provides parameters very useful for the classification of the pictures. PT

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Fig. 6. Filling a closed area.

A difficult problem, the search for the closed contours of blurred pictures, has been solved, where other methods, often more sophisticated, have failed on this application. The drawing of the closed contours of cloudy areas,

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Fig. 7. Photographic restitution of a train of cumulo-nimbus bank (visible image).

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152

V. LATTUATIand D. LEMOINE

realized on visible or infrared pictures has proved to be one of the most useful steps in view of the classification of meteorological situations. This method may also prove to be interesting in the identification of micro structures and certain textures, and will be used for other identification problems.

REFERENCES 1. J.S. Weszka, C. R. Dyer, L. Lieberman and A. Rosenfeld, A comparative study of texture measures for terrain classification, I.E.E.E. Trans. on Systems, man and cybernetics, Vol. SMC-6. 2. R. Bajscy and L. Lieberman, Texture gradient as a depth cue, Computer Graphics and Image Processing pp. 52-67 (1975). 3. M. Galloway, Texture analysis using gray level run lengths. Computer Graphics and Images ProcessinO, Vol. 4, pp. 172-179 (1975).

4. J. L. Merle, Some thoughts on textural discrimination by computer, Picture Processing and Psychopictoris pp. 371-379 (1970). 5. R.M. Haralick, K. Shanmugan and I. Dinstein, Textural features for image classification, I.E.E.E. Trans. on Systems, man and cybernetics, Vol. SMC-3, pp. 610-621 (1973). 6. J. S. Weszka and A. Rosenfeld, An application of texture analysis to materials inspection, Pattern Recognition 8, 195-199 (1976). 7. R. Bajscy, Computer identification of textural visual scenes. Memo AIM-18tYStanford University, (1972). 8. R. M. Haralick, Automatic remote sensor image processing. 9. M. Nagao, H. Tanabe and K. Ito, Agriculatural land use classification of aerial photographs (by Histogram similarity method). Pattern Recognition, pp. 669-671. 10. A. Rosenfeld and A. C. Kak, Digital Picture Processing. Academic Press. 11. W.K. Pratt, Digital Image Processing, pp. 471-513. John Wiley, New York.

About the Author--V. LATTUATIwas born in July 1942 in Paris, France. He received a 3rd cycle doctorate in electronics from the Orsay University in 1972. He is presently responsible for the Automatic Control Laboratory at the Conservatoire National des Arts et M6tiers, Paris, France. His research interests are image processing software/hardware, and currently, he is working on the development of 3-D perception systems for handling robots. About the Autbor--D. LEMOINEwas born in May 1948 in Paris, France. He graduated from Ecole Nationale Sup~rieure d'Arts et M6tiers (Paris) in 1970. He is presently a Research and Teaching Assistant in the Automatic Control Department at the Conservatoire National des Arts et M6tiers and Associate Professor at the Ecole Nationale Sup6rieure d'Arts et M6tiers. His research interests are pattern recognition and application of data processing methods to the Robotic areas.