Tensor Locality Preserving Projections Based Urban Building Areas Extraction from High-Resolution SAR Images

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1 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 Tensor Localty Preservng Proectons Based Urban Buldng Areas Extracton rom Hgh-Resoluton SAR Images Bo Cheng, Sha Cu, and Tng L Insttute o Remote Sensng and Dgtal Earth Chnese Academy o Scences, Chna Emal: {chengbo, cusa}@rad.ac.cn, lt010b@163.com texture normaton to extract urban buldng areas rom hgh-resoluton SAR mages. In 01, Xu et al. calculated eght knds o GLCM texture eatures o SAR mages [], on the bass o Bhattacharyya Dstance, Prncpal Component Analyss (PCA) was appled or selected texture eatures wth good recognton to reduce correlatons between derent texture eatures, then buldng areas are extracted accordng to grey level. It s well known that PCA and Independent Component Analyss (ICA) algorthm are more sutable to handle data set wth lnear structures [3]. However, SAR mages are aected by many complex actors such as target orentaton, ptch angle o platorm, polarzaton mode, wavelength and the electromagnetc envronment around the target. Moreover, these actors are not ndependent o each other. Especally, wth the mprovement o the spatal resoluton o SAR mages, the target contans abundant normaton, whch makes the nherent geometrc structure o data are o hghly complexty. Thereore, t s necessary to study new algorthms or the processng o ths knd o data set. In recent years, Manold Learnng [4] has been developed rapdly n the area o pattern recognton. It s a new method o machne learnng and provdes new drecton or dmensonalty reducton o hgh-dmensonal data set. Its goal s to nd the low dmensonal manold structure whch embeds n hgh dmensonal space and then gves an eectve low dmensonal embeddng coordnates. Accordng to ths concept, algorthms such as ISOMAP [5], Laplacan Egenmaps (LE) [6] and Localty Preservng Proectons (LPP) [7] were proposed. Currently, Manold Learnng algorthms whch were employed or eature extracton o SAR mages are mostly vector based. However, SAR mage s n tensor orm. It s necessary to convert rom tensor to vector orm beore eature extracton, ths very process would results n the "dmensonalty curse" and the loss o space geometry structure normaton. A seres o algorthms were tred to combne tensor analyss technology wth manold learnng theory, n order to solve those above mentoned problems, lke Sparse Tensor Embeddng (STE) [8], Local Tensor Subspace Algnment algorthm (LTESA) [9], Tensor Abstract Currently, the maorty o Manold Learnng algorthms appled or SAR mage eature extracton are vector based; the For tensor based SAR mages, a convert to vector: process has to be taken beore attrbute extracton. Durng ths process, curse o dmensonalty would be occurred and normaton o space geometry structure could be lost. Those phenomenon are not conducve or target recognton o SAR mages. In ths paper, Radarsat- mages were used as expermental data and the Tensor Localty Preservng Proectons (TLPP) algorthm was appled or the attrbute extracton o hgh-resoluton SAR mages, to mprove the recognton accuracy and acheve ast extracton o urban buldng areas. A comparson was made or the recognton results o TLPP and Localty Preservng Proectons (LPP). It s ound that that TLPP algorthm has a strong adaptablty o generalzaton, whch ndcates that TLPP can be eectvely used or ast extracton o urban buldng areas rom hgh-resoluton SAR mages, wth hgh accuracy. Index Terms synthetc aperture radar, manold learnng, eature extracton, tensor localty preservng proectons I. INTRODUCTION As an mportant part o urban land usage montorng, extracton o urban buldng areas plays a sgncant role n urban plannng management and mltary aars. Especally n the process o New Urbanzaton o Chna, wth the rapd ncrease o buldng areas, the realzaton o land supervson n a ast and eectve way becomes a maor techncal ssue. In recent years, wth the ast development o hgh-resoluton radar satellte, the acquston o all-tme all-weather data are convenent. In hgh-resoluton SAR mages, corner relector s ormed due to the multple reracton between buldngs and ground, whch results n hgh brghtness dsplay n mages; meanwhle, roads, plants and shadows darker tones alternatng wth brght spot o buldngs, those regularly arranged lght-and-shade generates dstngushed texture eatures[1]-[]. Researchers have taken advantage o Manuscrpt receved January 4, 016; revsed Aprl 1, 016. Ths work was supported by the Natonal Scence Foundaton o Chna under Grant No J. Adv. In. Technol. do: /at

2 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 Dscrmnatve Localty Algnment (TDLA) [10] and Sparse Tensor Algnment (STA) [11], etc. They have acheved good results n ace recognton and hyperspectral remote sensng mage classcaton. However, ew tensor based manold learnng algorthms researches have been conducted or the eature extracton o hgh-resoluton SAR mages. In ths study, TLPP [1]-[13] algorthm was appled or the ast and accurate extracton o urban buldng areas n complex scenes rom hgh-resoluton SAR mages. Frstly, wave ltraton was conducted to reduce the mpact o speckle nose on the extracton accuracy. Secondly, GLCM [14] texture eatures whch are conducve to the dentcaton o urban buldng areas were selected based on the prncple o separablty, and these selected texture eatures were calculated accordng to the optmal wndow sze whch was acheved by experment. Ater that, samples were traned by TLPP algorthm, to obtan an optmal proecton matrx whch was operated wth tranng sample and testng sample respectvely n order to reduce dmenson and acheve a new eature. Fnally, the new eature was taken as the nput o Otsu [15] method to extract urban buldng areas, and the nal result o buldng areas was acheved by post processng. At the same tme, TLPP algorthm was compared wth LPP algorthm to very the eectveness o TLPP algorthm. The results showed that TLPP algorthm has a strong generalzaton ablty. The new eature o test sample whch was acheved by drectly operatng wth the transormaton matrx obtaned based on the tranng sample showed better detecton eect. And detecton rates all exceeded 90%, wth low alse alarm rate. Thereore, TLPP algorthm s sutable or dmensonalty reducton o hgh-resoluton SAR mages and the new eature has good detecton capablty as well. II. METHODOLOGY In ths paper, extracton o urban buldng areas was studed as a pattern recognton problem. GLCM texture eatures wth TLPP algorthm were proposed or extractng urban buldng areas rom hgh-resoluton SAR mages. Ths method ncludes ollowng techncal approaches: wave ltraton, eature screenng, dmensonalty reducton, target classcaton and post-processng. Frstly, lterng o hgh-resoluton SAR mages was conducted by Lee algorthm. Secondly, GLCM texture eatures whch are conducve to the dentcaton o urban buldng areas were selected based on the prncple o separablty. Then the optmal wndow sze was acheved by experment, whch was used to calculate those selected texture eatures. Thrdly, tranng sample was traned by TLPP algorthm, obtanng an optmal proecton matrx whch was operated wth tranng sample and testng sample respectvely to reducng dmenson and acheve new eatures. Fnally, the new eature were used as the nput o Otsu method to extract buldng areas. Post-processng was also executed to acheve the nal results. A. Texture Features Based on Gray Level Co-occurrence Matrx (GLCM) GLCM based texture attrbutes extracton s one o the most classcal statstcal analyss methods. The concept o GLCM was proposed by Haralck n Its basc dea s to rstly calculate gray co-occurrence matrx, and then texture eatures were acqured through statstcal calculaton o the gray level co-occurrence matrx. Haralck proposed ourteen knds o texture eatures, where eght o them were commonly used, Energy, Contrast, Correlaton, Mean, Varance, Dssmlarty, Inverse Derence Moment and Homogenety. B. Tensor Localty Preservng Proectons TLPP s a tensor manold learnng algorthm, whch s put orward through ntroducng tensor analyss technque nto LPP algorthm. Assumng A A, A,, An s a 1 I1 I IN sample data set acqured rom the tensor space R. K M M R. And they embed n an unknown manold TLPP algorthm s then used to nd the best proecton transormaton matrx K, so that t can obtan the ntrnsc geometrcal propertes o the data set whle mantanng the local topologcal structure o M. The optmzaton obectve uncton o TLPP algorthm s dened as: arg mn Q U1,, U K,, B B W A U U A U U W 1 1 n n 1 1 n n The constrant condton s dened n ormula () n order to elmnate the arbtrary translaton. (1) A 1U1 nun D 1 () where, B A 1U1 nu and B n A 1U1 nu are n respectvely the coordnates o the data ponts A and A n the low dmensonal space. D s a dagonal matrx and s dened as D W. The purpose o usng the weght W s to ensure that the adacent sample ponts n the orgnal space are mapped as close as possble. W whch s an element o the smlarty matrx W s dened as: A A W exp( ), A N p( A )or A N p( A ) (3) t 0, others In ths ormula, A N p( A ) represents that A s n the nearest neghbor o A ; t s a postve constant; and the values o p and t are both emprcal values. 016 J. Adv. In. Technol. 9

3 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 Snce the optmzaton problem s a hgh order nonlnear programmng problem n the hgh order nonlnear constrant, calculatng every transormaton matrx drectly s not easble. Thereore, we solved ths problem by teratve method. Frst o all, assumng that U, U,, U, U,, U K are known and U s unknown. And 1,,, 1, 1,,, then U I K l B A U U U U (4) K K Accordng to the characterstcs o tensor and matrx trace, the optmzaton problem whch s shown n ormula (1) s converted to: III. DATA In ths paper, we took Radarsat- mages whose polarzaton mode s HH and spatal resoluton s 1.565m as the expermental data. And three sub mages o and were selected (Fg. 1). The sze o (c) s except or (a) and (b). Where, (a) was taken as tranng mage, (b) and (c) were taken as test mages. And eatures o targets n (c) are more complex than (b). And the expermental mages were ltered n order to reduce the eect o nose on the extracton accuracy o urban buldng areas. ( ) ( ) ( ) ( ) T T arg mn P U tr U B B B B W U (5), And the constrant condton s converted to: 1 T T tr U B B D U (6) where, B represents the -mode lattenng o Thus, the unknown transormaton matrx B. U can be obtaned by solvng the generalzed egenvalue equaton shown n the ormula (7). U s composed o eature vectors correspondng to the l smallest egenvalue o ths generalzed egenvalue equaton. The transormaton matrxes correspondng to other dmensons can be carred out n accordance wth ths method, untl the proecton transormaton matrxes are all calculated. T T B Y B B W U B B D U, For a low dmensonal embeddng o a K dmenson, the low dmensonal embeddng o data set, whch s shown n ormula (8), can be obtaned by these K transormaton matrxes. B A U U (8) 1 1 K K C. Threshold Segmentaton Based on Otsu Method Otsu Method s a threshold segmentaton algorthm proposed by Otsu n Ths method s o good segmentaton eects, strong real-tme perormance and wth ast operatonal speed. In ths study, Otsu method s appled to execute bnarzaton o the gray mage ormed by new eatures obtaned rom TLPP algorthm, or the dentcaton o urban buldng areas. The approaches are shown as ollow: (1) denty a threshold wthn the range o mage gray level to maxmum varance between two classes; () dvde mage nto buldng areas and non-buldng areas accordng to ths threshold, where the pxel wth gray value bgger than the threshold s regarded as buldng areas. (7) (a) Tranng mage (b) Test mage 1 (c) Test mage Fgure 1. Expermental mages. IV. RESULTS A. Selecton o Texture Features Snce these eght texture eatures contan derent normaton, t s necessary to screen texture eatures that are proptous to dstngush buldng areas and non-buldng areas. For ths purpose, we conducted an experment or eature selecton. Images showed n Fg. represent the orgnal mage and GLCM texture eature maps. And statstcal characterstcs o these texture eatures were shown n Table I. Accordng to Table I, on the bass o Separablty Prncple, Contrast, Correlaton, Mean, Varance and Dssmlarty were selected as the orgnal hgh dmensonalty eature set. (a) (b) (c) 016 J. Adv. In. Technol. 93

4 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 (d) (e) () (g) (h) () Fgure. Orgnal mage and texture eature mages. (a) Orgnal mage; (b) Energy; (c) Contrast; (d) Correlaton; (e) Mean; () Varance; (g) Dssmlarty; (h) Inverse Derence Moment; () Homogenety. TABLE I. STATISTICAL CHARACTERISTICS OF TEXTURE FEATURES related to drecton, step length, wndow sze and mage gray level. The nluence o wndow sze s the most pronounced one, ts approprate value needs to be conrmed accordng to the actual mage. In ths paper, t was determned by usng programmng calculaton and result analyss. Snce buldng areas, plant and water are man surace eatures n the cty, multple urban buldng areas (wth varyng buldng denstes), plant and water are chosen as expermental mages. Then we acheved the mean value o each texture eature. Ater that, we analyzed the trend o each texture eature curve to determne the optmal wndow sze. Taken Contrast as an example (Fg. 3): rstly, Contrast was calculated n derent wndow szes wth nterval o 4 between 3 3 and And the value o Contrast s normalzed. Then we analyzed the trend o the derence among buldng areas, plant and water wth the wndow sze to acheve the optmal wndow sze. Texture eatures Energy Contrast Correlaton Mean Varance Dssmlarty Inverse Derence Moment Homogenety Statstcal characterstcs It relects the unormty o the gray dstrbuton o the mage and the thckness o the texture. The value o Energy s larger n the homogeneous regon. Compared wth buldng areas, the values o plant and water are larger. However, the boundary o certan targets s not obvous so that cannot extract buldng areas eectvely. It can eectvely relect the contrast o the mage, extract the edge o the target and enhance the lnear structure. Snce buldng areas s composed o buldngs, roads, plant and shadow, regonal derences s large. So the value o buldng areas s hgher than plant and water. Thereore, t can well denty buldng areas. The value s hgher n homogeneous regons. In contrast to plant and water, the value o buldng areas s larger. It s the average gray value wthn the wndow and relects the unormty o gray dstrbuton. It ndcates devaton degree o each pxel wthn the wndow. And the value s larger n the regon wth larger gray change. The value o buldng areas s larger, compared wth plant and water. So t s n avor o dstngushng buldng areas and non-buldng areas. It s a statstcal parameter that expresses the vsual texture. It relects the contrast o gray level n the wndow, and t s helpul to the extracton o urban buldng area. It relects the unormty o the mage. Snce the derences o buldng areas, plant and water are not sgncant, t s dcult to dstngush buldng areas and non-buldng areas. It s also dcult to denty urban buldng areas or the same reason as Inverse Derence Moment. Fgure 3. Varaton trend o contrast wth the wndow sze. As shown n Fg. 3, the derences n the value o Contrast among buldng areas, plant and water ncrease sharply between the wndow sze o 3 and 7. The derences luctuated between the wndow sze o 7 and 35, but the overall trend s ncreasng. The derences ncreased slowly between the wndow sze o 35 and 43. (a) Fltered mages (b) Contrast (c)correlaton (d) Dssmlarty (e) Mean () Varance Fgure 4. The ltered mage and texture eature mages. B. Extracton o Texture Features Texture eatures were calculated pxel by pxel orm n movng wndow. Gray level co-occurrence matrx s Snce the purpose o ths study s extractng urban buldng areas, t s not necessary to relect the detals nsde urban buldng areas. Moreover, wth the ncreasng 016 J. Adv. In. Technol. 94

5 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 o the wndow sze, the edge o texture mages became more and more blurrng. Thereore, the wndow sze o s more approprate. Images showed n Fg. 4 are respectvely ltered mage and texture eature mages whch were calculated n the wndow sze o As we can see rom Fg. 4 that these texture eature mages are more helpul to recognton o urban buldng areas than the ntal mage. However, there s correlaton between these texture eature mages, so t s necessary to reduce dmenson. C. Dmensonalty Reducton o Texture Features In ths study, both experments wth TLPP algorthm and LPP algorthm are conducted to valdate the eect o TLPP algorthm. An optmal proecton transormaton matrx s respectvely obtaned or both o these two algorthms. And then a new eature s acheved by operaton between ths matrx and the hgh-dmensonal data set whch s ormed by ve texture eatures. And the new eature s normalzed to the range o 0 to 1 n order to acltate the extracton o urban buldng areas (Fg. 5). the accuracy o extracton o urban buldng areas (Table II). (a) TLPP algorthm (b) LPP algorthm Fgure 6. The extracton results o urban buldng areas. TABLE II. VALUES OF ACCURACY EVALUATION INDEXES (UNITS:%). Algorthm Expermental mages Detecton Rate False Alarm Rate Mssng Alarm Rate (a) TLPP algorthm (b) LPP algorthm Fgure 5. New eatures extracted by TLPP algorthm and LPP algorthm. Urban buldng areas are n brght colors and non-buldng areas are close to black tone n the mages ormed by the new eatures based on TLPP algorthm. However, or the new eatures based on LPP algorthm, urban buldng areas are n dark colors and non-buldng areas are n gray tone. Thereore, compared wth the LPP algorthm, the derence between derent classes n the new eatures based on TLPP algorthm s hgher, whch s more conducve to the dentcaton o urban buldng areas. D. Extracton o Urban Buldng Areas and Accuracy Evaluaton The new eatures obtaned were taken as the nput o Otsu method to extract urban buldng areas. Then post-processng was made to acheve the nal result o buldng areas whch was shown n Fg. 6. Fnally, Detecton Rate (DR) [16], False Alarm Rate (FAR) and Mssng Alarm Rate (MAR) were calculated to evaluate TLPP Tranng mage TLPP Test mage TLPP Test mage LPP Tranng mage LPP Test mage LPP Test mage As we can see rom Fg. 6 and Table II that the new eature o the tranng mage, acheved by TLPP algorthm, are n avor o extracton o urban buldng areas. Although False Alarm Rate s slghtly hgh, Detecton Rate also reached 90%. Thereore, TLPP algorthm can mprove recognton accuracy by comprehensve utlzaton o texture eatures and the spatal geometry o the mage. In the other hand, or test mages, Detecton Rate both reached 90%. Thus, TLPP algorthm has good generalzaton ablty. The transormaton matrx obtaned by tranng sample can be drectly appled to the dmensonalty reducton o the new samples (test mages), and the new eatures obtaned have good dscrmnaton. Recognton s acheved to some extent (average detecton rate reached 90%, wth low alse-alarm rate) rom new eatures o the tranng mage extracted by LPP algorthm. However, the new eatures o test mage 1 aled to dstngush buldng areas and non-buldng areas. Moreover, Detecton Rate o test mage s only 70.91%. t s concluded that an unavorable perormance o generalzaton s acheved by usng LPP algorthm. Ths s manly because the LPP algorthm s vector based, whch makes t necessary to transorm the tensor data set nto 016 J. Adv. In. Technol. 95

6 Journal o Advances n Inormaton Technology Vol. 7, No. 4, November 016 vector orm, durng ths process, spatal structure normaton o the orgnal data set s destroyed. The loss o assocaton normaton between derent data ponts caused the redundancy normaton and hgher order dependence o the orgnal data beng masked. We were unable to dg out the ntrnsc geometry o the data set, the access to the compact pattern wthn data s blocked. V. CONCLUSIONS In ths paper, we ntroduced Gray Level Co-occurrence Matrx based TLPP algorthm nto eature extracton, to reduce the redundancy among these eatures. A new eature was obtaned, whch was employed or extracton o urban buldng areas. A comparson was made between TLPP algorthm and LPP algorthm as well. The expermental results showed that Detecton Rates o the new eatures obtaned by TLPP algorthm all reached 90%, wth relatvely lower False Alarm Rate. It s also ound that n one o our experments, the new eature obtaned by LPP algorthm aled to denty urban buldng areas whle TLPP acheved good recognton. In ths study, TLPP algorthm successully avod the problem o curse o dmensonalty caused by the vector ormed data set, as well as makng ull use o tensor ormed eature textures and the spatal geometry o the data set to mprove the dentcaton accuracy. Moreover, TLPP algorthm has strong generalzaton ablty. Accordng to the proecton transormaton matrx obtaned rom the tranng sample, we can obtan new eatures wth good dscrmnaton wthout tranng new sample, whle achevng the goal o dmensonalty reducton o new samples. REFERENCES [1] L. J. Zhao, Y. L. Qn, G. Gao, et al., Detecton o bult-up areas rom hgh-resoluton SAR mages usng the GLCM textural analyss, Journal o Remote Sensng, vol. 13, no. 3, pp , 009. [] J. Xu, Y. Y. Chen, Q. H. Huang, et al., Bult-up areas extracton n hgh resoluton spaceborne SAR mage based on the ntegraton o grey and texture eatures, Remote Sensng Technology and Applcaton, vol. 7, no. 5, pp , 01. [3] L. Y. Zhou, W. T. Yu, and J. Y. Chen, et al., SAR mage sphercal local embeddng modelng and classcaton method, Journal o Sgnal Processng, vol. 9, no. 9, pp , 013. [4] H. S. Seung and D. D. Lee, The manold ways o percepton, Scence, vol. 90, no. 5500, pp , 000. [5] J. B. Tenenbaum, D. V. Slva, and J. C. Langord, A global geometrc ramework or nonlnear dmensonalty reducton, Scence, vol. 90, no. 5500, pp , 000. [6] M. Belkn and P. Nyog, Laplacan egenmaps or dmensonalty reducton and data representaton, Neural Computaton, vol. 15, no. 6, pp , 003. [7] X. F. He and P. Nyog, Localty Preservng Proectons[C]//Neural Inormaton Processng Systems, Cambrdge: MIT, vol. 16, pp , 004. [8] H. T. Zhao and S. Y. Sun, Sparse tensor embeddng based multspectral ace recognton, Neurocomputng, vol. 133, PP , 014. [9] S. S. Wu, X. Y. Jng, Z. S. We, et al., Learnng mage manold va local tensor subspace algnment, Neurocomputng, vol. 139, pp. -33, 014. [10] L. P. Zhang, L. F. Zhang, D. C. Tao, et al., Tensor dscrmnatve localty algnment or hyperspectral mage spectral spatal eature extracton, IEEE Transactons on Geoscence and Remote Sensng, vol. 51, no. 1, pp. 4-56, 013. [11] Z. H. La, W. K. Wong, Y. Xu, et al. Sparse algnment or robust tensor learnng, IEEE Transactons on Neural Networks and Learnng System, vol. 5, no. 10, pp , 014. [1] D. Zheng, X. Du, and L. Cu, Tensor localty preservng proectons or ace recognton, n Proc. 010 IEEE Internatonal Conerence on Systems Man and Cybernetcs (SMC), vol. 1, 010, pp [13] H. Zhao and S. Sun, Sparse tensor embeddng based multspectral ace recognton, Neurocomputng, vol. 133, pp , 014. [14] R. M. Haralck, K. Shanmugam, and I. Dnsten, Textural eatures or mage classcaton, IEEE Transactons on Systems, Man and Cybernetcs, vol. 6, pp , [15] N. A. Otsu, Threshold selecton method rom gray-level hstograms IEEE Transactons on Systems, Man and Cybernetcs, vol. 9, no. 1, pp. 6-66, [16] J. A. Shuelt, Perormance evaluaton and analyss o monocular buldng extracton rom aeral magery, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 1, no. 4, pp , Bo Cheng receved the PH.D. degrees n cartography and geographc normaton engneerng rom Chna Unversty o Geoscence(Beng) n 003. Snce then, He has been workng or 10 years n photogrammetry and remote sensng elds at Insttute o Remote Sensng and Dgtal Earth(RADI), Chnese Academy o Scences(CAS). He has actvely conductng research usng remote sensng and GIS n support o natural resource, urban land-use montorng, and envronmental applcatons Sha Cu studed n Lanzhou Unversty or a bachelor s degree rom 009 to 013. She attended Insttute o Remote Sensng and Dgtal Earth Chnese Academy o Scences n 013 or a master's degree and her drecton s remote sensng mage processng Tng L receved the bachelor o scence rom East Chna Insttute o Technology n 01.She attended Insttute o Remote Sensng and Dgtal Earth Chnese Academy o Scences n 01 or a master's degree and her research drecton s the applcaton o remote sensng technology and target recognton analyss. 016 J. Adv. In. Technol. 96