SAM and ANN classification of hyperspectral data of seminatural agriculture used areas

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1 Proceedings of the 28th EARSeL Symposium: Remote Sensing for a Changing Europe, Istambul, Turkey, June Millpress Science Publishers Zagajewski B., Olesiuk D., SAM and ANN classification of hyperspectral data of seminatural agriculture used areas SAM and ANN classification of hyperspectral data of seminatural agriculture used areas B. Zagajewski & D. Olesiuk University of Warsaw, Faculty of Geography and Regional Studies, Department of Geoinformatics and Remote Sensing, ul. Krakowskie Przedmiescie 30, Warszawa, Poland Keywords: ANN, SNNS, SAM, classification, hyperspectral data, plant communities, crops, land use, land cover ABSTRACT: The goal of this paper is methods and results presentation of artificial neural networks land cover classification based on DAIS 7915 hyperspectral data. Preparing the reference and training data were used SAM classification. To ANN classification experiments using variable pattern size were chosen two data sets included 13 bands obtained after MNF and 40 original bands. The best accuracy were achived using 13 MNF band with the 3x3 pixel subpattern size, what show useful of hyperspectral data extraction methods. 1 INTRODUCTION Vegetation cover is a perfect indicator of all other components of biosphere and should be well researched and mapped. This is possible, using hyperspectral data (which provides very good spectral, spatial and radiometric resolution), because plant species develop specific adaptations (pigment content, plant tissue structure etc.). These adaptations have direct impact on reflectance, which can be quantified using hyperspectral imagery. Application of remotely sensed techniques allows for vegetation research and mapping. One of the characteristics of vegetation is its variable reflectance: identification of some communities may be difficult using only spectral properties. Vegetation reflectance registered by remote sensing instruments is the average of the reflectance of photosynthetic active parts, non-photosynthetic active parts (i.e. branches, dry leaves), shadow and ground. These elements, begin an integral part of plant communities, impede their recognition in case of assuming their spectral properties. Thus, it is prepared obvious, that vegetation can be characterized by a high variability of the signal and therefore the statistical distribution of reflectance differs from the normal distribution. These relationship is frequently happen over the seminatural and agritulturally used areas, and traditional classification, that uses parametrical approaches does not show satisfying results. A method that uses artificial neural networks does not only depend on statistical parameters of particular class and hence makes it possible to include texture information as additional data. This method may be especially useful to separate and classify vegetation communities (Zagajewski et al. 2005). 2 STUDY AREA AND DATA SOURCES The study was conducted in the Low Beskid Mountains, which constitute one of the most natural ranges in the Carpathian Mts. in Poland (Fig. 1). The area is the Bystrzanka catchment extend form 49º34-49º41 N to 21º01-21º09 E. The area constitutes a mountain zone located at the altitude

2 range of m a. s. l. The region is agriculturally, but extensively used; with a domination of natural meadows and cereal and potato crops. DAIS 7915 hyperspectral data used in this study was acquired on 29 July 2002 with cooperation with the German Space Agency (DLR). This instrument is a 79-channel imaging spectrometer operates in the wavelength range 0,4-12,5µm with 15 bit radiometric resolution. After the preprocessing the resulting pixel size was 3 meters. During the overflight 3 lines of DAIS images were acquired (Fig. 1). Figure 1. DAIS 7915 images covering the study area. 3 METHODS SAM classification was used to verification land cover map performed in 2002 during the terrain mapping. Endmembers were gained from DAIS imagery (corresponding to the key polygons from the ground measurements). All the polygons represents each class on training area (Fig. 1) were used to learn ANN. Two data sets using in classification were extracted from 72 bands covering the optical region of the spectrum. First step was reduction by visual (with histogram) inspections bands with severe striping problems, receiving 50 bands. To create first data set, from this 50 bands were choosed 40

3 Proceedings of the 28th EARSeL Symposium: Remote Sensing for a Changing Europe, Istambul, Turkey, June Millpress Science Publishers Zagajewski B., Olesiuk D., SAM and ANN classification of hyperspectral data of seminatural agriculture used areas (1, 2, 3, 4, 7, 9, 10,11, 12, 14, 15, 16, 17, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 46, 47, 50, 52, 53, 55, 56, 58). Second step was compressing the information contained in originally 50 bands using MNF transformation. Thus, second data set was created using the 13 first bands after the MNF transformation. To classify land cover types were applied a multilayer, one-directional network, trained using a supervised method of back-propagation. In experiments were used two variable pattern size, what is available in the Stuttgart Neural Network Simulator (SNNS) software. To perform classification were applied per-pixel and 3x3 subpattern size (Fig. 2). In per-pixel process number of input nodes was depend of number of bands, but in 3x3 subpattern size window it must be augment by 9. To defining the number of hidden nodes were used the formula 3Ni+1. Every each class was classified separately, so output nodes was 1, and the value of training land cover polygons takes 1, and rest 0. The learning parameters was obtain from Kavzoglu and Mather (2003) papers: the initial weight range [-0.25, 0.25], learning rate 0.2, number of training samples Trained neural nets was testing on area showing on figure 1 as TES. Figure 2. Variable size pattern (source: SNNS user manual; modified). 4 RESULTS Overall accuracy assessment for three images are showed in table 1. Because 3x3 window size (including textural information) process gives better results than per-pixel, image 1 and 2 was classified only with this approaches. Table 2 and 3 showing training and test classification accuracy for image 3 in per-pixel and 3x3 subpattern size process for separately land cover types. Table 4 including confusion matrix of the best results achieved for image 1 using 13 MNF bands with 3x3 subpattern size window (Fig. 3). Table 1. Overal classification accuracy Accuracy 13 band MNF 1x1 13 band MNF 3x3 40 band 1x1 40 band 3x3 TRAINING TEST Image Overall accuracy [%] / Kappa Image / / / / Image / / Image / / Image / / / / Image / / Image / /

4 Figure. 3. Results of the DAIS 7915 data classification of the key poygon Biesnik N (1st flightline). Table 2. Classification accuracy of training area Accuracy 13 k MNF 1x1 13 k MNF 3x3 40 k 1x1 40 k 3x3 [%] Overall : 74.9 Overall : 75.9 Overall : 74.5 Overall : 73.7 Land cover Prod. User Prod. User Prod. User Prod. User Meadow Wastelands Coniferous fo Mixed forest Decidous fore Woodeds Orchard Beets Potatoes Oats Stubble Plough soil Asphalt road Dirt road Built-up area Buildings

5 Proceedings of the 28th EARSeL Symposium: Remote Sensing for a Changing Europe, Istambul, Turkey, June Millpress Science Publishers Zagajewski B., Olesiuk D., SAM and ANN classification of hyperspectral data of seminatural agriculture used areas Table 3. Classification accuracy of the test area 13 k MNF 1x1 13 k MNF 3x3 40 k 1x1 40 k 3x3 Accuracy Overall : 74.9 Overall : 75.9 Overall : 74.5 Overall : 73.7 [%] Prod. User Prod. User Prod. User Prod. User Land cover Meadow Wastelands Coniferous fo Mixed forest Decidous fore Woodeds Orchard Beets Potatoes Oats Stubble Plough soil Asphalt road Dirt road Built-up area Buildings Table 4. Confusion matrix of the test classification of the image data set. Expanation: Mea meadow, Dec decidous forest, Woo Woodeds, Orc orchard, Bee beets, Pot potatoes, Oat oats, Stu stubble, Asp asphalt road, Dir dirt road, B-u built-up area, Bui buildings. Class Mea Dec Woo Orc Bee Pot Oat Stu Asp Dir B-u Bui User accu. Mea Dec Woo Orc Bee Pot Oat Stu Asp Dir B-u Bui Prod. Accu

6 5 CONCLUSIONS Artificial neural network show potential for discriminating land cover types from hyperspectral imagery. Long training time is the most uncomfortable aspect of this classification. but data transformation methods like MNF can short the way to satisfied results. The best results are observed for: oat crops (99.8 %). stubbles (96.6 %). grasslands (94.8 %). deciduous forest (93.9 %). and the worst for tree clumps (38.8 %). orchards (44.7 %) and side roads (56.1 %). Textural window (3x3) and MNF compressed data (based on 50 bands) increase classification accuracy. ACKNOWLEDGEMENTS Interdisciplinary Centre for Mathematical and Computational Modelling Warsaw Uniwerity (ICM UW). for open access to high power computers. REFERENCES Kavzoglu T. & Mather P. M The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing. vol. 24. nr 23, 2003: Zagajewski B, Kozlowska A., Krowczynska M., Sobczak M. & Wrzesien M., Mapping high mountain vegetation using hyperspectral data. EARSeL eproceedings, 4(1) 2005:

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