Remte Sensing Water Infrmatin Extractin Based n eural etwrk Sensitivity Analysis Yanrng Wu 1,Da Sha 1,Cngcng Zhang 1,Chen Qi 1, Chen Ping 2* 1 Cllege f Infrmatin Science and Technlgy, Beijing rmal University 2 Centre f Infrmatin & etwrk, Beijing rmal University Beijing, China E-Mail: chenping@bnu.edu.cn *Crrespnding Authr: Chen Ping, chenping@bnu.edu.cn ABSTRACT Characteristics f each image r differences in the surrunding envirnment will cause the imaging f characteristics cannt keep necessarily balance. S, t avid the difference caused by using a unified mdel t extract the water unit, this paper presents a remte sensing water infrmatin extractin methd based n neural netwrk sensitivity analysis. First, Tchaban and Garsn methd are utilized t d sensitivity analysis n each band f remte sensing image, and then the bands which are relatively sensitive t the nrmalized difference water index (DWI) are btained. Then rati cmputing and threshld segmentatin are carried ut t extract the water infrmatin. Finally, an experimental test n remte sensing water infrmatin extractin by using ADSAT ETM+ remte sensing image data f ng Geer area WanZhngba Cunty in Tibet is cnducted. The water infrmatin using ur methd can be extracted accurately. KEYWORDS Sensitivity analysis; eural netwrk; Water infrmatin extractin; Remte sensing image 1 ITRODUCTIO Research n water extractin using remte sensing is carried ut early, and the applicatin level is als relatively thrugh [1]. Mst f typical water infrmatin extractin methds are mainly implemented n unified mdels t extract each water element. Hwever, in practical applicatins, different physical and chemical characteristics f each image, r differences in the surrunding envirnment will cause the imaging f characteristics cannt keep necessarily balance. Thus, using glbal unified mdel t extract each water element wuld lead t a certain deviatin frm the accurately extracted targets, and make the mistakes which culd have been avided r reduced riginally. Sensitivity analysis is a quantitative methd t describe the imprtant extent f the mdel input variables t utput variables [2]. Assume that sensitivity analysis mdel dented as (1), x=(x 1, x 2,, x n ) is the set f input parameters. Making each parameter varies in pssible range f values t study and predict the degree t which an input parameter affects the mdel utput [3]. y f x, x..., x ). (1) ( 1 2 n Recent years, sensitivity analysis has been applied int varius fields. Fr example, in the research f investment prject evaluatin, sensitivity analysis can calculate the net present value (PV), with the internal rate f return (IRR) and ther ecnmic indicatrs t prvide reference fr investment decisins [4]. Faced with cmplicated eclgical system, utilizing sensitivity analysis can select parameters which play a leading rle in eclgical mdel, and pay attentin t these parameters at eclgical research and cnservatin [5]. Further Mre, 1 atinal atural Science Fundatin f China (41072245,41272359), Specialized Research Fund fr the Dctral Prgram f Higher Educatin f China (20120003110032 ), Fundamental Research Funds fr the Central Universities(2012ZD05) ISB: 978-1-941968-31-4 2016 SDIWC 1
sensitivity analysis prvides a reliable basis fr engineering t calculate reasnably and imprves efficiency. Accrding t the result f sensitivity analysis, it culd guide the cntrl f the gelgical survey and cnstructin quality t facilitate the verificatin and evaluatin f structure safety [6]. evertheless, in earth science which includes many kinds f parameters, the applicatin f sensitivity analysis is rare, just simple statistic, regressin, etc. While neural netwrk has strng learning ability and adaptive capacity s that it can better reflect the nnlinear relatinship between every tw parameters, the purpse f this paper is t illustrate the utilizatin and meaning f sensitivity analysis based n neural netwrk in remte sensing infrmatin extractin. At first, use sensitivity analysis based n neural netwrk t calculate the sensitivity cefficient f each band f remte sensing image. Then pick up the bands whse sensitivity cefficient are relatively high r lw thrugh rati calculatin t enhance cntrast. In the end, threshld segmentatin is carried ut t extract the water infrmatin precisely. 2 SESITIVITY AAYSIS BASED O EURA ETWORKHEADIGS Suppsing that the neural netwrk is a threelayer frward etwrk,,, M represent the number f neurns in the input layer, the hidden layer and the utput layer respectively. (x 1,, x ) is the input variable. (y 1,, y M ) is the utput variable. In (2), w represents the cnnectin weight between neurns in the hidden layer and the utput layer. w. (2) w ij In (3), v represents the cnnectin weight between neurns in the hidden layer and the utput layer. v. (3) v jk m f(net j ) and f(net k ) represent the activatin functin f hidden layer active neurn j and the activatin functin f the utput layer active neurn k severally. 2.1 Garsn Algrithm[7] The impact f input variables n the utput variable using Garsn Algrithm is: Q ik j1 i1 1 w ijv jkg w r1 rj. w v g ij jk j w r 1 rj ( i 1,..., k 1,..., M). As the values f cnnectin weight w ij and v j k can be psitive r negative, Z ik which represents the numeratr f Q ik will weaken the impact f xi n y k. (4) Z ( w v g / w ). (5) ik j1 ij jk r1 Similarly, the Values f Q ik als can be psitive r negative, s the relative sensitivity cefficient f xi n y k cannt be calculated by Q ik. As a result, it is n sense t srt the results calculated by this frmula. In this paper, we transfrm the frmula prpsed by Garsn, and the transfrmed frmula can reflect the relative influence extent (relative sensitivity) f xi n y k. And we nted Q ik as the sensitivity cefficient f xi t y k. ( w v ( w v g / g / i1 j1 ij jk r1 rj w ) rj. w ) ' j1 ij jk r1 Q (6) ik rj ISB: 978-1-941968-31-4 2016 SDIWC 2
( i 1,..., k 1,..., M). When the utput variable is fixed (k is fixed), we can srt by each sensitivity cefficient f the input variable x i t the utput variable y k. 2.2 Tchaban Algrithm[8] Equatin (7) represents the impact f the input neurn i n the hidden neurn j, and O j is the utput frm the hidden neurn j. xiwij S. ij (7) Equatin (8) represents the impact f the hidden neurn j n the utput f the neurn k, O k as well as y k represents the value f the utput frm neurn j. j jv jk S. (8) jk Equatin (9) defines the sensitivity f the input value x i n the utput value y k. k xiwij jv jk xi wp ik. j1 w j1 ijv jk (9) y j 3 IFORMATIO EXTRACTIO METHOD 3.1 Water Index Calculatin k Water bdy has a strng absrptin t sunlight, indicating a weak reflectivity cmpared with ther terrestrial infrmatin in general. Hwever, the spectral characteristics f water bdy in every wavelength are quite different. Usually, frm the visible t the mid- -infrared band, the reflectin f water bdy is gradually k weakened, which reaches the strngest level in the range f near- infrared and mid-infrared wavelength. Accrding t the spectral characteristics f water bdy, the rati calculatin was used t establish and develp water index mde which enhances the infrmatin f water bdy [9,10]. The cmputatin f DWI (rmalized Difference Water Index) is mre sensitive t changes in water bdy infrmatin than vegetatin canpies, while it s effective t distinguish between water bdy infrmatin and shadw tgether with ther infrmatin. 3.2 Infrmatin Extractin Prcess Based n neural netwrk sensitivity analysis, water infrmatin extractin prcess is shwn in Fig. 1: Data f ETM+ Remte sensing image in all bands in all bands Cefficient f water bdy (DWI) Input variables Output variables Sensitivity analysis based n neural netwrk Srt Select High sensitivity cefficient bands/w sensitivity cefficient bands Threshld segmentatin Water bdy infrmatin Figure 1. Water bdy infrmatin extractin prcess based n neural netwrk sensitivity analysis ISB: 978-1-941968-31-4 2016 SDIWC 3
4 EXPERIMETA RESUTS AD AAYSIS The experimental data is derived frm ADSAT ETM+image (Fig. 2) f ng Geer area WanZhngba Cunty in Tibet. Six kinds f wave bands were implemented, respectively TM1, TM2, TM3, TM4, TM5 and TM7. All f them keep the spatial reslutin f 15 meters. Fr extracting water bdy infrmatin based n neural netwrk sensitivity analysis, ur experiment aims t prve the validity f the methd in this paper by extracting water bdy infrmatin frm images. (a) water bdy equalizing value (b) water bdy variance (c) nn-water bdy equalizing value Figure 2. Pseud clr image cmpsed by 1-5, 7 bands f remte sensing image data in ng Geer area Wanzhngba Cunty in Tibet Fig. 3 demnstrates the equalizing value histgrams and variance histgrams f the water bdy, the nn-water bdy and all wave bands in experimental area, and B1-B6 crrespnd t the TM1-5, 7 bands. (d) nn-water bdy variance ISB: 978-1-941968-31-4 2016 SDIWC 4
(e) experimental area equalizing value (f) experimental area variance Figure 3. The equalizing value and variance histgram f water bdy, nn-water bdy and experimental area As is shwn in Fig. 3(a), Fig. 3(b), the equalizing value and variance f the water bdy in the TM2, TM1 band are much higher than ther bands, pwerfully illustrating that water infrmatin is mainly cncentrated in TM2, TM1 bands. And cmpared t Fig. 3(c)-Fig. 3(f), it is bvius that the equalizing value f the nn-water bdy and experimental area are rughly equal in each band, while in the TM1, TM2 bands the variance is lw, demnstrating that the water bdy infrmatin in TM1, TM2 is quite valuable. ext, we use Garsn methd and Tchaban methd t calculate the sensitivity cefficients f the water bdy in each band in experimental area. And the results are shwn in Fig. 4: (b) Tchaban methd sensitivity cefficient Figure 4. Sensitivity cefficient histgram Frm Fig. 4(a) and Fig. 4(b), we can cnclude that as fr Garsn and Tchaban methd, the sensitivity cefficients f the TM4, 5 band are relatively higher, while the TM1, 2 band are lwer. In cnsideratin f the effects f TM1, 2 band n water bdy infrmatin, this paper divides bands by the values f sensitivity cefficients. Meanwhile, in rder t increase the difference between the bands, we divide (TM4+TM5) by (TM1+TM2), and execute threshld segmentatin finally. Band peratin and threshld segmentatin are realized by EVI4.6, and the water bdy infrmatin is extracted as shwn in Fig. 5. (a) Garsn methd sensitivity cefficient Figure 5. Extracted water bdy infrmatin Cntrasted with the riginal image (Fig. 2), we learn that by using the methd based n neural netwrk sensitivity analysis we can find ut the ISB: 978-1-941968-31-4 2016 SDIWC 5
sensitive bands. In additin, by extracting the water bdy infrmatin, we can acquire an accurate distributin f the water bdy area aviding the influence f ther factrs such as shadw. The methd based n neural netwrk sensitivity analysis can effectively pick ut the sensitivity factrs cnnected with remte sensing water bdy infrmatin. 5 COCUSIO Aiming at extracting water bdy infrmatin f remte sensing images, we prpse a methd f extracting remte sensing water infrmatin based n neural netwrk sensitivity analysis. Utilizing Garsn and Tchaban methd t cmplete sensitivity analysis, cmbined with the calculatin prcess f water bdy index (DWI). Further Mre, using ADSAT ETM+ remte sensing image data f ng Geer area Wanzhngba Cunty in Tibet as data surce fr extractin, regrading rivers and lakes extractin as experimental purpses, we btain high accuracy infrmatin extractin results. The result f experiment further demnstrate that the sensitivity analysis can efficaciusly extract water bdy infrmatin. And the methd als cntributes t the accurate extractin f the vegetatin, desert, wetland and ther special subjects. [4] Brgnv E, Peccati, Uncertainty and glbal sensitivity analysis in the evaluatin f investment prjects, J f Prductin Ecnmics. 104 (2006) 62. [5] Xu Chnggang, Hu Yuanman, Cheng Yu, Jiang Yan, i Xiuzhen, Bu Renchang, He Hngshi, Sensitivity analysis in eclgical mdeling, Chinese Jurnal f Applied Eclgy. 15 (6) (2004) 1056. [6] Xia Yuanyu, Xing Haifeng, Sensibility analysis f slpe stability based n artificial neural netwrk, Chinese Jurnal f Rck Mechanics and Engineering. 23 (16) (2004) 2703. [7] Garsn G D, Interpreting neural t netwrk cnnectin weights, AI Expert. 6(4) (1991) 47. [8] Tchaban T, Taylr M J, Griffin A. Establishing impact s f the inputs in a feed frward neural netwrk. eural Cmpute Appl. 7 (1998) 309. [9] Cheng Qia, Jiancheng u, Yngwei Sheng, Zhanfeng Shen, Zhiwen Zhu, Dngping Ming, An adaptive water extractin methd frm remte sensing image based n DWI, Jurnal f the Indian Sciety f Remte Sensing. 40(3) (2012) 421-433. [10] McFeeters S K, The use f nrmalized difference water index (DWI) in the delineatin f pen water features. Internatinal Jurnal f Remte Sensing. 17(7) (1996) 1425-1432. REFERECES [1] u Jian-cheng, Sheng Yng-wei, Shen Zhan-feng, i Jun-li, Ga-i-jing, Autmatic and high-precise extractin fr water infrmatin frm multispectral images with the step-by-step iterative transfrmatin mechanism, Jurnal f Remte Sensing. 13(4) (2009) 610-615. [2] Xian i, ngcang Shu, ihng iu, Dan Yin, Jinmei Wen, Sensitivity analysis f grundwater level in Jinci Spring Basin(China) based n artificial neural netwrk mdeling, Hydrgelgy Jurnal. 20 (2012) 727. [3] Emanuele Brgnv, Elmar Plischke, Sensitivity analysis: A review f recent advances, Eurpean Jurnal f Operatinal Research. 248 (2016) 869. ISB: 978-1-941968-31-4 2016 SDIWC 6