Unstructured Human Settlement Mapping with SAR sensors Fabio Dell’Acqua, Mattia Stasolla and Paolo Gamba Department of Electronics, University of Pavia Via Ferrata, 1, 27100 Pavia, Italy Corresponding author:
[email protected] Abstract— In this paper human settlement detection using SAR data is addressed, with stress on informal settlement analysis. We show that, even using coarse spatial resolution SAR data, it is possible to discriminate between structured and unstructured settlements. In particular, two approaches are proposed: a supervised procedure to exploit spatial patterns and backscatter intensity to discriminate between structured and unstructured kinds of human settlements, and an unsupervised procedure to detect human settlements in large area SAR images.
I. I NTRODUCTION Unstructured human settlement mapping and monitoring is an important topic for many national and international initiatives including the European Global Monitoring for Environment and Security (GMES) initiative and the humanitarian and development aid policies of the United Nations. Also, monitoring settlements is useful to acquire information on phenomena like illegal immigration that are very high-ranked on the list of issues for policy makers. The application of these techniques has a global scope and would be particularly relevant for the developing world. Unstructured (informal) human settlements are usually defined as dense settlements comprising communities housed in self- constructed shelters under conditions of informal or traditional land tenure [1] They are common features of developing countries and are typically the product of an urgent need for shelter by poor people, especially in an urban context. These areas are characterized by rapid, unstructured and unplanned development. On a global scale, informal settlements are a significant problem especially in third world countries that are housing the world’s disadvantaged. Continuous migration flows have largely contributed to an increase of the unstructured built-up areas. One of the main effects of such a situation is the transformation of settlement structures, and no viable way is now available for an extensive, efficient monitoring of these areas except remote sensing. So, informal settlement monitoring, as a part of remote security monitoring, will be definitely one hot topic in the research scenario in the next 5 to 10 years. Observation of informal settlements is therefore a primary issue in security-centered global monitoring. However, for spatial technology to be effective in informal settlement environments, it has to be cheap, both in data acquisition and processing, as automated as possible to achieve faster and
more reliable results, simple to use and largely based on tested routines and algorithms. While this issue may be addressed by optical VHR satellites, developing procedures for processing SAR data on these environments is still a very interesting research topic. In fact, the acquisition of spatial data in informal settlements has been so far mainly based on conventional ground mapping techniques or conventional photogrammetric approaches applied to nonphotogrammetric satellite data. Maps are compiled using analogue or analytical photogrammetric methods [2]. These are, to a large extent, manual operations and require a wide expertise; moreover, they are slow and biased by the operator skills. The aim of this research is to develop a semi-automatic algorithm for the extraction of unstructured settlements, analysis of settlement density and, if possible, characterization of trends and change detection. The new point in which research is currently needed is the use of SAR data for this task. Indeed, it is expected that the future fine spatial resolution SAR sensors (first of all TerraSAR-X and RADARSAT-2) will help in the accurate delineation of the urban-to-rural border and to a rough but very timely characterization of the typology of settlements. More precisely, two examples of SAR data for settlement monitoring will be provided in the following, using two different methods. The first example (section II) deals with refugee camp mapping in North Sudan, particularly around the town of Al Fashir, whose surroundings saw a very large sprawl of tent fields in August 2004 due to the humanitarian emergency situation in the region. The second example (section III) is targeted at targeted at single-image, rapid detection of smaller human settlements for more effective water management in sub-Saharan countries (the example image refers to a portion of Senegal). II. S TRUCTURED AND UNSTRUCTURED SETTLEMENT DISCRIMINATION
In order to extract human settlements in SAR images, the technique developed in our research lab in the past years is based on a supervised neural network classification chain, with spectral and spatial analysis steps [3]. Additionally, spatial analysis may be performed using textural features, possibly extracted using a locally adaptive window width [4]. For the present application, the Fuzzy Artmap neural classifier is used to discriminate between urban and desert areas, taking care of separating the city from the refugee camp.
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Fig. 1. SAR images used in this research work to exemplify unstructured and structured human settlement mapping: (a,b) ASAR images of Al Fashir (Sudan); (c) ASAR image of a part of Senegal.
Three classes are thus specified: city, desert/rocks and tents. It must be noticed that in fig. 1(a) and (b) structured human settlements (parts of the Al Fashir urban area) are clearly visible, while the refugee camp (circled in green) has lower backscatter levels. Unfortunately, deserted areas and rocks also have low backscatter levels. Therefore, it is expected that backscatter intensity alone might not be enough to discriminate a more structured urban area from an informal settlement like the refugee tent camp, in an efficient way. More refined results may come from an analysis including spatial relationships between intensity values. To this aim, textural features are the best candidates. However, many options should be explored. First of all, the original SAR data set may or may not be preliminarily processed with a despeckling filter, whose window width should be chosen according to the spatial extents of the classes in the test area. then, the window width for the textural feature computation should be chosen, according to the spatial relationships inside each class. As a consequence, the general procedure proposed in the above mentioned papers had to be adapted to the structured versus unstructured settlement discrimination problem, and the relevant parameters, together with the more significant results, will be shown in Section IV. III. R APID DETECTION OF HUMAN SETTLEMENTS The second method was developed with a focus on rapid detection and the use of a single image to save the time and money required to form an interferometric pair. The procedure is based on three steps: 1) First, a selection of the areas eligible to be urban is performed by picking pixels whose values are higher than
an “ad hoc” threshold. A series of morphologic filtering operations fills holes and removes isolated pixels. The non-eligible areas are masked out. 2) Then, three co-occurence texture maps are computed, namely, mean, dissimilarity, correlation, using the smallest (δx =+1,δy =+1) spatial displacement, given the coarse resolution of the image. 3) Finally, an ISODATA classification is performed on the co-occurrence map. Generally, the urban areas are described by the pixels assigned to a limited number of classes, or even just one class. Unfortunately, the class selection is still to be done manually. The effect of the procedure is the definition of main location for human settlements, discarding, thanks to the spatial refinement step, small isolated scatterers and linear structures related with man-made structures such as railways or elevated highways. Some problems however still remain in sensorfacing slopes, where as a byproduct of the foreshortening effect, remarkably high returns produce pixel values close to those typical of urban areas. Research is in progress on the use of morphological methods to discriminate those false positives based on their elongated shape. IV. EXPERIMENTAL RESULTS The two procedures were tested on SAR imagery of human settlements areas recorded by the sensors on board of ENVISAT. All ASAR images, while recorded with the finest acquisition mode, have a rather coarse spatial resolution for urban applications, and their usefulness is therefore somehow limited. However, these results are a “proof of concept” for higher resolution and multiple polarization data soon to become available after the launch of the TerraSAR-X and Cosmo/skymed Low Earth Orbit (LEO) satellites.
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Fig. 2. Results and data about Al-Fashir (Sudan): (a) joint classification of the two ASAR images, after de-noising; (b) joint Classification using textural features (mean, second moment, variance; (c) (d) joint classification of ASAR and SPOT textural features; (d) manual ground truth of the area (yellow = desert, red = structured settlements, green = tent camp.
The data on Al Fashir were recorded by the ENVISAR ASAR sensor on the 26th of July and the 13th of August, 2004. Moreover, a 5 m-resolution SPOT image was considered. It was acquired in August, 13th (2003), earlier than refugee camp set up. Thus, there is no additional information about it, but its use is based on the idea that SPOT finer spatial resolution may improve the city classification, and therefore the overall accuracy. ASAR data for the Al-Fashir areas are depicted in fig. 1(a) and (b). Classification maps computed using the methodology proposed in Section II from the original data are very noisy, because of speckle noise. Thus, adaptive filters (Lee, Frost, Kuan and Gamma) were used and best performances - both in accuracy and processing time - were reached using a 27×27 window Gamma filter. Nevertheless, the resulting map shows some confusion between city boundaries and camp and between city and soil. The reason is in similar DN values of these types of surfaces: in the first case confusion is to be expected, since city borders are less dense and with lower reflectance (the boundary between city and camp is not so clear-cut); in the second case the misclassification is bound up with SAR sensors’ properties, which usually associate settlements to other ground features, like sediments or rocks. Despite the aforementioned encouraging result, which can be sufficient for our purposes, we calculated image textures, in order both to reduce speckle and to exploit spatial correlations in the scene. Best performances are obtained using two sets of textures (mean-second moment-variance or mean-entropyvariance-dissimilarity) computed using a 21 × 21 window.
In order to try and improve the results, a multisensor/multitemporal analysis was carried out, as the SPOT image was co-registered to ASAR ones and then jointly classified. Generally speaking this data fusion technique is very useful, since information provided by an individual sensor can be incomplete, inconsistent or imprecise. In the particular case of human settlement extraction, there is a strong complementarity of SAR and optical data [5]. In fact, SAR images have a limited spatial resolution and they are dependent on geometry,
materials and orientation of settlement structures, while optical images suffer from atmospheric conditions, but have high spatial and spectral resolution. Unfortunately, in this case, the SPOT image was recorded before the SAR ones, when no tent camp was in place. As a result, the lack of optical information about the camp does not allow to improve its detection results. Moreover, the use of SPOT data will not improve the separability of city and soil classes, since pixels have very similar mean and standard deviation values even in the optical and near infra-red bands. This is clearly shown by the maps in fig. 2, where the desert is depicted in yellow, the main urban area in red and the refugee camp in green. Fig. 2(a) is obtained using ASAR backscatter data only, while in fig. 2(b) textural features computed from the SAR data are used to exploit spatial relations and reduce salt-and-pepper classification noise. Fig. 2(c) shows the results for joint ASAR and SPOT classification, again based on textural features. All these maps should be compared with the manually extracted ground truth in fig. 2(d), and the corresponding confusion matrices are reported in Table I. As for the Senegal area, the data were recorded by the ASAR instrument aboard the ENVISAT satellite on the 15th of May, 2005. The whole image is depicted in fig. 1(c). After applying the unsupervised procedure presented in Section III to the area in the data, many human settlement were detected, and their existence and boundaries were tested using available information and on-line image databases. As a result, in fig. 3 a couple of samples of the original data and the corresponding detection results are shown. It may be observed that the urban areas were actually detected, and very few false positives are reported. This is also due to the nearly total absence of the unfavorable mountain environment (see sect. III). V. CONCLUSIONS In this paper we propose a supervised method based on fuzzy classification and an unsupervised method based on ISODATA clustering procedure to detect informal settlements in SAR images, possibly together with optical images.
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Fig. 3. Two samples of the Senegal data set and the extracted human settlements’ locations: (a) and (c) SAR samples showing the towns of Sadio and Kaffrine, respectively; (b),d) corresponding extracted locations using the unsupervised procedure proposed in section III.
TABLE I Q UANTITATIVE EVALUATION AND COMPARISON OF THE RESULTS FOR THE A L -FASHIR TEST AREA . Fig. 2(a) 88.34% 87.31% 72.42% Overall Accuracy Fig. 2(b) Fig. 2(a) 86.19% 89.05% 86.57% Overall Accuracy Fig. 2(c) 72.70% 92.59% 80.86% Overall Accuracy
City 157741 19946 2716 87.44% 87.27%
Desert 4151 538462 1348 98.99%
Camp 16668 58299 10669 12.46%
City 153909 29203 89 84.01% 88.37% City 129805 7982 4 94.20% 87.98%
Desert 7074 549148 1889 98.39%
Camp 17577 38356 12755 18.57%
Desert 42077 571003 2813 92.71%
Camp 6678 37738 11900 21.13%
ACKNOWLEDGMENT The authors would like to thank Paolo Balducci for performing the computations required to produce the results for the Senegal image. R EFERENCES [1] H. Ruther, H. Martine, and E.G. Mtalo, “Application of snakes and dynamic programming optimisation technique in modelling of buildings in informal settlement areas”, ISPRS J. Photogrammetry Remote Sens., vol. 56, pp. 269-282, 2002. [2] S.O. Mason and C.S. Fraser, “Image sources for informal settlement management”, Photogrammetric Record, vol. 16, no. 92, pp. 313-330, 1998. [3] P. Gamba, F. DellAcqua: “Improved multiband urban classification using a neuro-fuzzy classifier,” Int. J. Remote Sens., Vol. 24, n. 4, pp. 827-834, Feb. 2003. [4] P. Gamba, F. Dell’Acqua, G. Trianni: “Semi-automatic choice of scaledependent features for satellite SAR image classification”, Pattern Recognition Letters, Vol. 27, n. 4, pp. 244-251, Mar. 2006. [5] L. Fatone, P. Maponi, and F. Zirilli, “Fusion of SAR/Optical images to detect urban areas”, IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Rome, Italy, Oct. 2001, pp. 217-221.
The results of the first technique show that ASAR data, after appropriate filtering, can precisely separate city areas from refugee camps and tents. Nevertheless, in the presence of statistically similar classes, they are limited by scarce resolution. Also SPOT sensor, if we consider raw images, is not suited for our aim. The introduction of textures, instead, considerably improves results, especially in differentiating settlements from ground. It may be observed that the urban areas were actually detected, and very few false positives are reported. This is also due to the nearly total absence of the unfavorable mountain environment (see sect. III). Future works will be based on VHR SAR sensors (RADARSAT or TerraSAR-X), which will give the possibility of analyzing settlements’ growth and changes at a finer scale, as well with the aid of VHR optical images like those by Quickbird or Ikonos satellites.
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