A Robust Forest Height Estimation by using EBPNN by Utilizing Morphological Estimation

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1 A Robust Forest Height Estimation by using EBPNN by Utilizing Morphological Estimation Sheta Rani #1, Mahima Jain *2 # M.Tech. Scholar in Computer Science and Engineering Department, RGTU University # Prof.in Computer Science and Engineering Department, RGTU University Bhopal India Abstract Forests are of significance as natural carbon sink in climate change mitigation and the global carbon cycle. The interferometric coherence and height can be used for land use monitoring as ell as for estimation of biophysical parameters, like aboveground biomass of forests. This paper has developed an approach hich automatically accept input image and classify the vegetation regions. Here use of morphological filter and bilinear filtration is done for increasing the efficiency of the neural netork training. Experiment is done on real images of Tandem-X, Biotope, etc. Results shos that proposed ork is better as compare to previous ork on different evaluation parameters. Keyords Image Processing, Forest Height, SAR Image, TANDEMX. I INTRODUCTION Forest above-ground dry biomass (AGB, hereith simply referred to as biomass) is an important variable for the global carbon budget, not only due to the uptake of carbon dioxide in the process of photosynthesis, but also because forests store huge amounts of carbon, hich are eventually released into the atmosphere folloing a disturbance [1]. Accurate and timely mapping of forest AGB is therefore crucial to support carbon cycle modeling. Traditional methods based on forest inventories and aerial photography, and more recently, LiDAR campaigns, give accurate estimates of AGB; hoever, such methods are expensive and become inefficient henever frequent and large-scale mapping is needed. Therefore, there is a need for development of alternative methods for frequent and large-scale biomass mapping [2]. One of the more promising techniques for above-ground dry biomass mapping is Synthetic Aperture Radar (SAR), cf. [3]. Being an active sensor, radar is independent of eather and external illumination. Space borne SAR missions currently in operation are characterized by an image resolution on the order of meters. In addition, interferometric SAR, In SAR, offers the possibility to exploit to further observables besides the radar backscatter, namely the coherence and the interferometric phase. These are affected by the forest structure and, thus, are related to forest variables such as tree height, and stem volume or AGB. In a single-pass acquisition scenario, the association beteen In SAR observables and forest variables is expected to be maximized because temporal de-correlation can be assumed to be negligible. Experimental evidence on the suitability of single-pass In SAR to estimate forest variables at X-band (avelength of approximately 3 cm) as provided by data acquired by airborne sensors [4 6], and during the Shuttle Radar Topography Mission (SRTM) [7]. In June 2010, the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) satellite as launched. Together ith the almost identical tinsatellite TerraSAR-X (launched in June 2007), the first satellite-based single-pass SAR interferometer as formed. In the bistatic mode of the TanDEM-X mission (consisting of the TanDEM-X and TerraSAR-X satellites), only one satellite is used for ISSN: Page 147

2 transmission hile both satellites are used for reception. For simplicity, e ill refer to this mission as the TDM mission. In TDM data, temporal de-correlation is limited to a minimum because of the small along-track baseline beteen the sensors. The primary objective of TDM is to obtain a global Digital Elevation Model (DEM) ith an absolute height accuracy better than 10 m and an equatorial spatial resolution of 12 m [8]. Because of the limited penetration of microaves into the canopy, X-band interferograms over forests are characterized by an elevation offset hich is dependent on forest canopy height and density [9]. This offset suggests exploiting TDM imagery to estimate tree height although a reference for the ground elevation is needed. Since X-band microaves do not significantly penetrate the closed canopy of a dense forest, a Digital Terrain Model (DTM) for the ground surface needs to be provided by some other, independent method, for example P-band SAR [10], or LiDAR [6,7]. Besides forest height estimation, retrieval of above-ground dry biomass as also investigated in some studies. In [10], a Root Mean Square Error (RMSE) of 46.1 Mg/ha (biomass range up to 360 Mg/ha) as obtained for biomass in a tropical forest using airborne SAR in X- and P-band, and in [7] RMSE = 19% as obtained using SRTM in X-band. II. Related Work Hurtado (2012) evaluated this approach ith TanDEM-X InSAR data and found the R2 to be 0.62 for the interferometric forest height estimation of ALS height. The performance of biomass estimation from Pol-InSAR data as tested in Neumann et al. (2012). Airborne P- and L-band data ere used ith an RVoG model approach at the Sedish test site Krycklan and the most successful estimations ere found ith L-band data. It as found that the intensity at HH-VV as more sensitive to biomass than any other polarization at L-band. In contradiction to some earlier reported studies, it as also found that the incidence angle and topography dependence had a large impact on the results. This might be due to the fact that P- and L-band data ere utilized, here the ground contribution and therefore its topography becomes more pronounced. In Solberg et al. (2013), TanDEM-X InSAR data ere used to evaluate the interferometric height sensitivity to spruce tree volume and biomass. They found the stem volume and AGB to be proportional to the interferometric height, ith 19-20% RMSE at stand-level. A crucial finding in this study as the possibly linear relation beteen the interferometric height and stem volume, as this stands in contradiction to earlier studies claiming curvilinear relationships (Askne et al. 1997; Mette et al. 2004; Woodhouse 2006). A possible explanation given as that stand volume and AGB might be linearly related to the canopy height hile it could be curvilinearly related to the mean tree height and top height (H100; recall Section 1.1.1). Arnaubec et al. (2014) evaluated the precision of vegetation height estimations hen an RVoG model as applied to P-band data at different or many polarizations. It as found that a loss in vegetation height precision could be calculated, independent of estimation method, hen derived from an adaptation of the Cramer-Rao bound. It is possible that a similar theoretical derivation could be done for X-band data. Kugler et al. (2014) check the performance of compact Pol-InSAR ith TanDEM-X data as thoroughly, here forest height as the primary estimated parameter. They evaluated single- and dual-polarization cases against ALS forest heights by applying a to-layer RVoG model, at the test ISSN: Page 148

3 sites Krycklan (in northern Seden), Traunstein (in southern Germany) and Maas (a tropical test site in Indonesia). The loest RMSE as found at the Sedish test site, ith 1.58 m and ith r 2 =0.91 for the single-polarization inversion. The dualpolarization inversion as noisier but still had an RMSE=2.02 m and r 2 =0.86. They noticed a topographic influence on the inversion performance at the Krycklan test site. It as concluded that the correlation beteen the SPC and tree top as strong but varied ith seasonal and environmental changes. They only noticed eak effects of the incidence angle on the penetration, but in general the penetration as surprisingly high for being from X- band. It as not made clear if the deep penetration as due to actual penetration through the vegetation volume or due to gaps in the vegetation layer. III. Proposed Work Here proposed ork focus on classifying the vegetation and forest region from the input data image. Classification is done on the basis of filter image and input vector. Error Back propagation neural netork is use for classification. Whole ork is explained in belo block diagram. DTM Image Dataset Pre-Process Water Body Masking Bilinear Interpolation Morphological Filter DTM Forest Pre-process Read a image means making a matrix of the same dimension of the image then fill the matrix correspond to the pixel value of the image at the cell in the matrix. This can be understand as the let belo image consist of four pixel having dimension of 2X2 then for this image a matrix is of same Training of EBPNN Fig. 1 Block Diagram of proposed modal = 12 5 Fig. 5.4 Read each pixel.value of the image. ISSN: Page 149

4 dimension 2X2 and its four cell contain value as per the pixel color and representing format. In this step image is resize in fix dimension. As different image have different dimension. So conversion of each is done in this step. This can be understand as if one image have an dimension of the 30X30 and other image has the dimension of 29X28 then it need to resize it either in 30X30, so that it matrix operation can be easily perform on both matrix. One more ork is to convert all images in gray format. A different image has RGB, HSV, etc. format so orking on single format is required. Water Body Masking To avoid signal disturbance and false elevation values of natural and artificial ater bodies, a ater mask of all ater bodies larger than 2000 m2 as derived from the biotope map. The threshold as set because small streams like drainage ditches shoed no or only slight height disturbance effects in the idem. Finally, all areas covered by the ater mask ere excluded from further analyses. Bilinear Filtration Bilinear Interpolation is a simple interpolation technique in hich e fill the gaps beteen pixels using the neighbor pixels. For example, e have an unknon pixel in beteen four pixels, and let s say the unknon pixel is f(x,y) and it is surrounded by four pixels hich are: Q 11 = (x 1, y 1 )., Q 12 = (x 1, y 2 ), Q 21 = (x 2, y 1 ), Q 22 = (x 2, y 2 ). All these four neighbor pixels are knon, no by using Bilinear Interpolation e can find the values of this unknon pixel. No, first of all, move in the x direction only. The formula used for Bilinear Interpolation for x factor is Morphological Filter (Disaggregated Progressive Morphological Filter) MF, the input image is arranged by applying a structuring element B in hich a pixel value is compared and altered in accordance to the values of its neighborhood folloing the provided rules. These rules consist of to basic operators, namely, erosion and dilation. Therefore, each pixel image as disaggregated ith a divisor of 3 and locally interpolated by three-point linear interpolation. This operations consists of convoluting an image B ith some kernel (B), hich can have any shape or size, usually a square or circle. The kernel B has a defined anchor point, usually being the center of the kernel. As the kernel B is scanned over the image, e compute the maximal pixel value overlapped by B and replace the image pixel in the anchor point position ith that maximal value. As you can deduce, this maximizing operation causes bright regions ithin an image to gro (therefore the namedilation). Take as an example the image above. Applying dilation e can get: Training of Error Back Propagation Neural Netork In this step input vector from both DTM is push in the neural netork ith its proper class. ISSN: Page 150

5 Netork activation Forard Step, Error propagation Backard Step Consider a netork of three layers. Let us use i to represent nodes in input layer, j to represent nodes in hidden layer and k represent nodes in output layer. refers to eight of connection beteen a node in input layer and node in hidden layer. The folloing equation is used to derive the output value Yj of node j Yj here, X j = x i. - j, 1 i n; n is the number of inputs to node j, and j is threshold for node j The error of output neuron k after the activation of the netork on the n-th training example (x(n), d(n)) is: e k (n) = d k (n) y k (n) The netork error is the sum of the squared errors of the output neurons: 1 e E(n) 1 X j e 2 k (n) The Backprop eight update rule is based on the gradient descent method: It takes a step in the direction yielding the maximum decrease of the netork error E. This direction is the opposite of the gradient of E. Iteration of the Backprop algorithm is usually terminated hen the sum of squares of errors of the output values for all training data in an epoch is less than some threshold such as Testing of EBPNN: In this step input query image is preprocess as done in the training module, similarly feature vector is create. Finally feature vector is input in the EBPNN hich give output. No analysis of that output is done that hether specified class is desired one or not. IV. Experiment and Results Dataset Lider Tandem-X - E The total mean squared error is the average of the netork errors of the training examples. E AV 1 N N n 1 E(n) Ultracamx Biotope ISSN: Page 151

6 Table 5. Comparison of Mean elevation values. Techniques Root Mean Square Error Forest Level Urban Like DTM [12] Ground Truth Results Table 3. Comparison of Mean elevation values. Techniques Mean Elevation Forest Level Urban Like DTM [12] Proposed Work (Neural Netork) From table 3 it is obtained that proposed ork is Here it is shon that use of tandem-x image ith trained neural netork for height estimation of vegetation in forest or urban areas region is quite Table 4. Comparison of Mean elevation values Techniques Mean Average error Forest Level Urban Like DTM [12] Proposed Work (Neural Netork) From table 4 it is obtained that proposed ork is Here it is shon that use of neural netork for classification of bare and vegetation region is quite Proposed Work (Neural Netork) From table 5 it is obtained that proposed ork is Here it is shon that use of neural netork for classification of bare and vegetation region is quite Table 6. Comparison of Mean elevation values. Techniques Accuracy Forest Level Urban Like DTM [12] Proposed Work (Neural Netork) From table 6 it is obtained that proposed ork is Here it is shon that use of neural netork for classification of bare and vegetation region is quite V. CONCLUSIONS In this paper a ne approach of forest height estimation techniques is explain ith their requirement area. Here use of neural netork help in reading the ne areas of the input image and identify the vegetation region. Here this paper has made the necessary changes in previous ork for proper training of the neural netork. Experiment is done on real images of TandemX, Biotope, etc. Results shos that proposed ork is better as compare to previous ork on different evaluation ISSN: Page 152

7 parameters. There is alays ork remain in future ork as height estimation accuracy can be further be increase by using color correction algorithms. REFERENCES 1. M. Santoro, J. Askne, G. Smith, and J. E. S. Fransson, Stem volume retrieval in boreal forests from ERS-1/2 interferometry, Remote Sens. Environ., vol. 81, no. 1, pp , Arnaubec, A. et al., Compact PolInSAR and Homogeneous Random Volume Over Ground Model. IEEE Transactions on Geoscience and Remote Sensing, 52(3), pp Caicoya, A.T. et al., Boreal forest biomass classification ith TanDEM-X. In IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany, July, 2012, pp Hurtado, D.M., Interferometric Processing of TanDEM-X Images for Forest Height Estimation. Aalto University. 5. Kugler, F. et al., TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Transactions on Geoscience and Remote Sensing, 52(10), pp Praks, J. et al., Boreal forest tree height estimation from interferometric TanDEM-X images. In IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany, July, 2012, pp Solberg, S. et al., Estimating spruce and pine biomass ith interferometric X-band SAR. Remote Sensing of Environment, 114(10), pp Solberg, S. et al., Monitoring spruce volume and biomass ith InSAR data from TanDEM-X. Remote Sensing of Environment, 139, pp Fensham, R.J. and Fairfax, R.J., Aerial photography for assessing vegetation change: a revie of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4): Bovolo, F. and Bruzzone, L., A detailpreserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(12): Hyyppä, H., Yu, X., Hyyppä, J., Kaartinen, H., Honkavaara, E. and Rönnholm, P., 2005a. Factors affecting the quality of DTM generation in forested areas. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI(Part 3/W19) pp Thies, M. and Spiecker, H., Evaluation and future prospects of terrestrial laser scanning for standardized forest inventories. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI, Part 8/W2 pp Johannes Schreyer, Christian Geiß, Member, IEEE, and Tobia Lakes TanDEM-X for Large-Area Modeling of Urban Height: Evidence from Berlin, Germany IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. ISSN: Page 153