ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE

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1 ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE Min Chen a, b,, *, Qing Zhu a, b,, Shengzhi Huang a, b,, Han Hu a, Jingxue Wang a a Faulty of Geosienes and Environmental Engineering, Southwest Jiaotong University, Chengdu, 6756, China minhen@home.swjtu.edu.n; zhuq66@63.net; shzhhuang3@63.om; huhan@whu.edu.n; xiaoxue86@63.om b State-provine Joint Engineering Laboratory of Spatial Information Tehnology for High-speed Railway Safety, Chengdu, 6003, China Collaborative Innovation Center for Rail Transport Safety, Chengdu, 6003, China Commission III, WG III/ KEY WORDS: Low-altitude remote sensing, Image mathing, Loal region, Feature similarity onfidene ABSTRACT: Improving the mathing reliability of low-altitude images is one of the most hallenging issues in reent years, partiularly for images with large viewpoint variation. In this study, an approah for low-altitude remote sensing image mathing that is robust to the geometri transformation aused by viewpoint hange is proposed. First, multiresolution loal regions are extrated from the images and eah loal region is normalized to a irular area based on a transformation. Seond, interest points are deteted and lustered into loal regions. The feature area of eah interest point is determined under the onstraint of the loal region whih the point belongs to. Then, a desriptor is omputed for eah interest point by using the lassial sale invariant feature transform (SIFT). Finally, a feature mathing strategy is proposed on the basis of feature similarity onfidene to obtain reliable mathes. Experimental results show that the proposed method provides signifiant improvements in the number of orret mathes ompared with other traditional methods.. INTRODUCTION In reent years, omprehensive low-altitude remote sensing platforms, suh as unmanned aerial vehile (UAV), have provided new possibilities for high-resolution image aquisition and have been extensively used in many appliations (Bulatov et al., 0; Choi et al., 0; Colomina et al., 04; Wallae et al., 04a, 04b; Gonalves et al., 05; Zhou et al., 05). Compared with traditional approahes (e.g., satellite remote sensing and aerial photogrammetry), low-altitude remote sensing platforms have the following inherent advantages: first, the work mode is flexible, effiient, and less affeted by weather, and they an take off any time as tasked; seond, they are able to obtain large-sale and high-preision remote sensing images; and third, the overlapping degree between images is relatively large. It an enhane the reliability of the subsequent proessing. The ost of platform onstrution, maintenane, and operation is also low. Although low-altitude remote sensing platforms have many advantages in image aquisition, the image proessing tehnology is still unable to meet the needs of many appliations. Therefore, investigating automati low-altitude remote sensing image proessing tehnology, in whih robust image mathing is a fundamental issue, is neessary. Image mathing is a key omponent of many tasks in photogrammetry and remote sensing, whih is extensively used in many appliations, suh as image registration (Song et al., 00; Gruen et al., 0; Cheng et al., 03; Chen et al., 04; Li et al., 04), stithing (Brown et al., 007), and 3D reonstrution (Kratohvil et al., 00). Image mathing methods an be mainly lassified into two ategories, namely, intensity-based methods and feature-based methods (Xiong et al., 00). In intensity-based methods, many approahes are based on ross-orrelation (Zhao et al., 006; Karna et al., 008), whih is a simple onept and easy to implement. The main problem with ross-orrelation is that it is diffiult to deal with image deformations exept for shift transform. Therefore, ross-orrelation-based methods are usually used to math epipolar images. Many mathing frameworks, suh as the least squares (Gruen et al., 005) and relaxation mathing tehniques (Zhang et al., 007), have been proposed to improve the reliability of remote sensing image mathing. In addition to ross-orrelation, researhers have proposed a lass of frequeny domain mathing methods based on Fourier spetrum (Zitova et al., 003). This type of method searhes the best math by using image frequeny domain information. Compared with the ross-orrelation-based methods, the frequeny domain mathing methods based on Fourier spetrum an obtain better mathing results under image illumination hange and noise interferene. Other well-known intensitybased mathing methods adopt mutual information to find mathes (Viola et al., 997; Thevenaz et al., 998). The basi idea of mutual-information-based mathing methods is to keep moving the target window in the searh area. When the mutual information between the target window and the searh window ahieves a maximum value, the target window enter and the searh window enter are regarded as one pair of math. Therefore, the mathing problem an be onverted into the omputation of the mutual information maximum value. In image mathing, the number of feature-based methods far exeeds that of intensity-based methods. In feature-based methods, those based on loal invariant feature desriptor are * Corresponding author doi:0.594/isprsannals-iii

2 the most well-known. In general, the framework of suh methods inludes three steps, namely, feature detetion, desription, and mathing. In the field of omputer vision and pattern reognition, researhers have proposed many wellknown loal invariant feature detetors (Harris et al., 988; Smith et al., 997; Lowe, 999; Matas et al., 004; Mikolajzyk et al., 004) and desriptors (Belongie et al., 00; Mikolajzyk et al., 005; Bay et al., 008; Calonder et al., 00; Rublee et al., 0). One of the most well-known methods is the sale invariant feature transform (SIFT) (Lowe, 004). The SIFT method has been extensively used in many appliations beause of its robustness for image rotation, sale hange, and a ertain degree of viewpoint and illumination hange. In addition to investigating invariant desriptors, researhers have enhaned image mathing performane by improving the mathing strategy (Morel et al., 009; Yu et al., 0). Given that featurebased methods have made remarkable ahievements in omputer vision and pattern reognition, they are used to math remote sensing images (Li et al., 009; Lingua et al., 009; Huang et al., 00; Sedaghat et al., 0; Wang et al., 0). The harateristis of low-altitude platforms, i.e., bumpy and low height, result in failure of the ommonly used mathing methods to math low-altitude remote sensing images. Although many methods an solve image rotation and sale hange well, they annot obtain satisfatory results when large viewpoint hange exists, as shown in Figure. the advantages and disadvantages of the proposed method and further improvements that an be made.. OVERALL METHODOLOGICAL CONSIDERATION Projetion transformation between different pixel pairs is different beause of the large objet depth variation in lowaltitude remote sensing images. All the pixel pairs within the whole images do not meet the same affine transformation. We divided the images into loal regions to overome this problem. The transformation between the pixels in the orresponding regions an be approximated to an affine transform beause the depth variation in the loal region is small (see Figure ). (a) transformation between the whole images (a) Figure. Low-altitude images with viewpoint hange: (a) referene image. (b) test image. A robust mathing method is proposed in this study to improve the mathing performane of low-altitude remote sensing images, partiularly for those images with large viewpoint hange. First, multiresolution loal regions are extrated from the whole image, and a geometri transform is implemented on the extrated regions. Based on this transformation, the viewpoint hange between images is onverted into rotation and sale hange. Seond, point features are deteted inside and outside of the loal regions. Then, the viewpoint invariant feature area is omputed for eah interest point based on the onstraint of loal regions. In this proedure, the size of the feature area is determined aording to image resolution instead of feature sale value. Then, the SIFT method is adopted to alulate the desriptor. Finally, feature similarity onfidene is defined and a mathing strategy based on it is presented to find feature orrespondenes. The remainder of this paper is organized as follows: Setion presents the overall methodologial onsideration of how the proposed algorithm will improve the mathing performane. Setion 3 presents the proposed low-altitude remote sensing image mathing algorithm in detail. The experimental results, along with the detailed analysis and disussion, are presented in Setion 4. The final setion onludes the paper by disussing (b) (b) transformation between loal regions Figure. Image geometri transformation. Based on the previously presented analysis, the proposed mathing method is onduted through the following three steps: First, the loal regions are extrated from the input images and are normalized based on a transformation. Seond, the interest points are deteted and desribed based on the loal region onstraint. Third, desriptor similarity onfidene is defined and a mathing strategy is proposed based on the onept. The proedure is summarized in Figure 3. Referene image Test image Loal regions extration and transformation Point feature detetion and desription under the onstraint of loal regions Feature mathing based on desriptor similarity onfidene Mathing result Figure 3. Flowhart of the proposed method. doi:0.594/isprsannals-iii

3 3. THE PROPOSED IMAGE MATCHING METHOD 3. Loal Regions Extration and Transformation Region detetors are adopted to obtain the loal regions. In this study, given that loal regions are used to onstrain the subsequent different viewpoint image mathing, the seleted region detetor should be robust to image viewpoint hange. In the study onduted by Mikolajzyk et al. (005b), they demonstrated that the maximally stable extremal region (MSER) detetor (Matas et al., 004) is more robust than other region detetors for image viewpoint hange. Therefore, the MSER detetor is adopted in this study to extrat the loal regions. The initial irregular regions extrated by using the MSER detetor have been fitted to elliptial regions based on the region seond-order moment to failitate the subsequent proessing. The original MSER detetor is not sale invariant. In this study, we improved the multiresolution strategy (Forssen et al., 007) and integrated it with MSER to obtain multiresolution loal regions. To do this, a Gaussian sale pyramid is onstruted by image blurring and subsampling with a series of Gaussian kernels. Then, loal regions are deteted separately at eah resolution image by using the original MSER detetor. Finally, dupliate regions from different sale images are removed by eliminating fine-sale MSERs. The following three riteria are used to distinguish dupliate MSERs: ) Distane between the entroids of the two MSER elliptial regions should be smaller than 4 pixels. ) The value of abs S S max S, S should be less than 0., where S and S are the sizes of the two regions. 3) The value of abs max L, L should be less than 4, where and are the diretions of the major axis of the two elliptial regions and L and L are the perimeters of the two ellipses. The transformation between orresponding loal regions is approximated to affine transform. In this study, a transform is proposed to map the affine hange into rotation and sale hange to improve the feature repeatability rate. We assume that EAp is the loal elliptial region, l is the length of the major axis, w is the length of the minor axis, is the orientation of the major axis, and CAp is the irular region transformed from EAp. The radius of CAp is r l w. If matrix T denotes the transformation between EAp and CAp, then T satisfies the following equation: T T X X T X X r, () where X is a point on the ellipse and X is the region enter. Sine X is loated on the ellipse, we obtain the following equation: T X X μ X X, () where, ;, μ is the seond-order moment of 0 0 EAp. Matrix T an be alulated from Equations () and () as follows: 0 0 r T Then, the loal elliptial region an be transformed to a irular region based on the following equation: CA p p 0 (3) T EA. (4) The affine transformation between the elliptial regions has been onverted into the sale and rotation transformation after the aforementioned proesses: If the transformation between the orresponding transformed regions is B, then we obtain the following equation (Hartley et al., 000): where μ and L T μl B μrb, (5) μ R denote the seond-order moment of the orresponding transformed regions. For the two transformed regions, we derive the following expression: μl LE μr RE. (6) where E denotes an identity matrix. From Equations (5) and (6), we an derive the following equation: where and L T L R E B B. (7) R are sale fators. B is a matrix inluding sale and rotation fators. Therefore, only rotation and sale hange exist between the orresponding transformed regions. The transformation between the transformed regions an be expressed as follows: where B H R. (8) H denotes a sale transformation. rotation transformation. 3. Point Feature Detetion and Desription R denotes a Considering the rotation and sale hange between the transformed loal regions, the differene of Gaussians (DoG) detetor is adopted to extrat the point features from the loal regions. In pratial appliations, the resolution of the inputted low-altitude remote sensing image is known. Therefore, for eah extrated point feature, the feature area size is determined based on the image resolution instead of the feature sale value. doi:0.594/isprsannals-iii

4 We assume that the resolution of the two inputted images is and (, unit: meter). The feature area radius of all the features in the image with resolution is set as follows: 6, 4, r 3, 0. 64, 0. (9) The feature area radius of all features in another image with resolution is set as follows: r r (0) This strategy an overome the unreliability of feature sale omputation. After point feature detetion, the well-known SIFT method is adopted to ompute the feature desriptor. Given that the extrated loal regions annot over the whole image, the point features from the outside area of loal regions should be deteted and desribed. In this study, the following approah is proposed: ) The DoG detetor is used to find interest points and obtain feature oordinates from the outside area of the loal regions. ) The deteted interest points are lustered into loal regions. The distanes between eah interest point to all loal region enters are omputed. If the ratio between the smallest distane d and the seond smallest distane d is less than a threshold t, then the interest point will be lustered into the loal region orresponding to the smallest distane d. Otherwise, the interest point is regarded as two features and they are lustered into the nearest two regions, as shown in Figure 4. Figure 4. Interest points lustering rule. 3) The feature area of eah interest point is omputed aording to the elliptial parameters of the region in whih the interest point is lustered. Then, the SIFT method is adopted to desribe eah feature. The flowhart of the feature area omputation is summarized in Figure 5. Figure 5. The flowhart of feature area omputation. 3.3 Feature Mathing Based on Similarity Confidene In this paper, the similarity onfidene is defined as follows: For a feature p with desriptor D p on the referene image, we ompute the Eulidean distane between D p and the desriptors of all the features ( q, q ) on the test image. We find the losest feature and their desriptors are, n q i and the seond losest feature D qi and q j, D qj, respetively. Then, the similarity onfidene C between feature p and q i is expressed as follows:, i p, q, i p q j C p q dist d d dist d d, () where dist is a funtion used to ompute the Eulidean distane between two desriptors. From the previously presented definition, the similarity onfidene is only omputed between eah feature on the referene image and its losest feature on the test image. Based on the similarity onfidene, a robust feature mathing method is proposed in this study, whih is implemented as follows: Step : A set used to save final mathes is marked as SetFinal, whih is initially an empty set. doi:0.594/isprsannals-iii

5 Step : The Eulidean distane between all the features on the referene image and all the features on the test image are omputed. Step 3: For eah feature on the referene image, its orresponding losest feature on the test image is determined. These feature pairs are regarded as andidate mathes and are saved into another set SetAll. Meanwhile, the similarity onfidene of eah feature pair is omputed. HesAff (Hessian-Affine) (Mikolajzyk, 004) and MSER (Matas, et al., 004) detetors ombined with the most wellknown SIFT desriptor (Lowe, 004). The proposed method was also ompared with the state-of-the-art Affine-SIFT (ASIFT) (Morel et al., 009) and iterative SIFT (ISIFT) (Yu et al., 0) methods. Step 4: A threshold T is set. Those feature pairs with similarity onfidene onforming to C T are saved into a set SetC. Step 5: An affine transformation H is estimated in SetC by using the random sample onsensus (RANSAC) algorithm. In SetC, those feature pairs onforming to H are saved into sets SetCfitH and SetFinal at the same time. Then, we reset SetC, suh that SetC SetC SetCfitH. Figure 6. Dataset: low-altitude UAV images. Image size is pixels. Image resolution is 0.05m. Step 6: The affine transformation H is adopted to verify the feature pairs in SetAll. Those pairs onforming to H are saved into sets SetAllfitH and SetFinal at the same time. Then, we reset SetAll, suh that SetAll SetAll SetAllfitH. Step 7: An iteration algorithm is implemented for Steps 5 and 6 until the following expression is true: setall or Num setc 4 or Numiteration 5, () Figure 7. Dataset : low-altitude oblique images. Image size is pixels. Image resolution is 0.08m. where NumsetC denotes the number of feature pairs in SetC and Numiteration denotes the number of iterations. Step 8: All mathes in SetFinal are outputted as initial mathing result. In the initial mathing result, there are several outliers inevitably. In traditional methods, the RANSAC algorithm is used to estimate the affine transform between images. Those mathes that do not onform to this transform will be eliminated as outliers. The algorithm performs well for middle- or lowresolution remote sensing images beause the transformation between all pixels of the whole inputted images an be approximated to an affine transform. However, for low-altitude remote sensing images, several orret mathes are eliminated beause the transformation between the whole inputted images annot be approximated to an affine transform. An epipolar onstraint based on the fundamental matrix is used to eliminate outliers with the RANSAC algorithm to overome this problem. 4. Experimental Data 4. EXPERIMENT In the experiments, the three pairs of low-altitude remote sensing images shown in Figures 6 to 8 were used to evaluate the performane of the proposed method. We ompared the proposed method with traditional methods by using the DoG (Lowe, 999), HarAff (Harris-Affine) (Mikolajzyk, 004), Figure 8. Dataset 3: low-altitude oblique images. Image size is pixels. Image resolution is 0.08m. 4. Mathing Results In the experiments, parameters of all the aforementioned methods in our experiments were set aording to their reommended values in the original referenes. Table summarizes the performane of eah method in terms of number of orret mathes. In Table, the four methods that ombined the HarAff, HesAff, MSER, and DoG detetors and SIFT desriptor obtained fewer orret mathes than those methods that improved the mathing framework, namely, ASIFT, ISIFT, and the proposed method. More speifially, in all the three group experiments, the proposed mathing method obtained more orret mathes than all the other methods. Although the proposed method is based on MSERs, it detets and mathes features from the whole images rather than MSER mathes. Therefore, the proposed method performs well when the orret mathes of the MSER method are few. doi:0.594/isprsannals-iii

6 Mathing methods Experimental data Dataset Dataset Dataset 3 HarAff HesAff MSER SIFT ASIFT ISIFT 44 4 Proposed Table. Number of orret mathes of different methods on the three datasets. loal regions an be approximated to an affine transform whether the transformation between the whole images onform to an affine transform or not. Thus, the proposed method an obtain satisfatory mathing results when the ISIFT method fails. Some mathing results in our experiments are shown in Figures 9 to. In the results, mathes are linked with white lines. The performane of the ISIFT method is better than that of the SIFT method beause the ISIFT method is an iterative method. The ISIFT method estimates the transformation between images and builds a simulation of one of the inputted images. Then, feature detetion and mathing are onduted between another inputted image and the simulated image based on the original SIFT method. However, in low-altitude remote sensing image mathing, the transformation between the whole inputted images annot be approximated to an affine transform. The simulation of one image generated in the iteration of the ISIFT method an only orrespond to a partial area of another image. Therefore, its performane improvement relative to the SIFT method is limited. The ASIFT method performs generally well (only ranking behind the proposed method). The method improves the view invariane of the SIFT method by simulating and building the affine spae of inputted images. For image viewpoint variation, several images in the affine spae of one inputted image have a pose similar to the images in the affine spae of another inputted image. Good mathing results an be obtained between these simulated images. Although the ASIFT method an improve the mathing performane under view hange, its appliation is limited beause the transformation between the inputted images is not onsidered and an exhaustive strategy is needed for the feature searh. Thus, the time effiieny of the ASIFT method is an issue of onern. The simulated affine spae in the ASIFT method is disontinuous, and the mathing result an be improved by dereasing the sample interval. For low-altitude remote sensing images, several regions annot be overed in the affine spae beause of the topographi relief, whih is why the number of orret mathes generated by the ASIFT method is less than that of the proposed method in the experimental results. (a) HarAff (b) HesAff () MSER (d) SIFT (e) ASIFT (f) ISIFT (g) Proposed method Figure 9. Mathing results based on Dataset. (a) HarAff (b) HesAff () MSER (d) SIFT Similar to the ASIFT and ISIFT methods, the proposed method also improves the mathing performane by simulating images. The differenes are as follows: () The ASIFT method simulates images without onsidering the transformation between images. In our proposed method, eah loal image region orresponds to a simulated irular image area. It an avoid deteting and mathing features in a mass of useless images (images without orrespondene). Besides, the proposed method simulates eah region with different transform. These regions an over the input image better and failitate more mathes. (e) ASIFT (f) ISIFT (g) Proposed method Figure 0. Mathing results based on Dataset. () The ISIFT method simulates the whole image with one transformation. In our proposed method, the simulation proess is implemented to loal regions. The transformation between the doi:0.594/isprsannals-iii

7 Laboratory for Surfae Transportation Weather Impats Prevention (No. NELXX060). REFERENCES Bay H., Ess A., Tuytelaars T. et al., 008. Speeded-up robust features (SURF). Comput. Vis. Image Und., 0(3), pp (a) HarAff (b) HesAff () MSER (d) SIFT Belongie S., Malik J., and Puziha J., 00. Shape Mathing and Objet Reognition Using Shape Contexts. IEEE T. Pattern Anal., 4(4), pp Brown M., and Lowe D. G., 007. Automati panorami image stithing using invariant features. Int. J. Comput. Vis., 74(), pp Bulatov D., Solbrig P., Gross H., et al., 0. Context-based urban terrain reonstrution from UAV-videos for geoinformation appliation. In: The International Arhives of the Photogrammetry, Remote Sensing and Spatial Information Sienes, XXXVIII-/C, pp (e) ASIFT (f) ISIFT (g) Proposed method Figure. Mathing results based on Dataset CONCLUSION In this study, we proposed a novel point feature mathing method for low-altitude remote sensing images based on the analysis of image geometri transformation. The ontribution of this study lies in the following aspets: () A new mathing framework for low-altitude remote sensing images is proposed based on the loal region onstraint and feature similarity onfidene. The proposed method an effetively math images with large viewpoint differene. () Compared with the state-of-the-art mathing method ASIFT, only one geometri transformation is implemented in our method for eah loal region, ensuring satisfatory results and time effiieny. The experimental results showed that the proposed method performs better than the other methods for low-altitude remote sensing images with viewpoint variation. However, the loal region extration in the proposed method highly depends on the image ontent, whih indiates that the proposed mathing method works better for strutured images. A possible future work is to improve the loal region extration method and make the proposed mathing method perform well in strutured and textured areas. ACKNOWLEDGEMENTS This work was supported by the National Natural Siene Foundation of China (No and 44730), the Siene and Tehnology Program of Sihuan, China (No. 05SZ0046), the Open Researh Fund by Sihuan Engineering Researh Center for Emergeny Mapping & Disaster Redution (No. K05B006), the Open Researh Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. (4) Key 03), and the Opening Researh Fund of National Engineering Calonder M., Lepetit V., Streha C., et al., 00. BRIEF: Binary Robust Independent Elementary Features. In Proeedings of European Conferene on Computer Vision. 05-, September, Heraklion, Greee, pp Chen Q., Wang S., Wang B., et al., 04. Automati Registration Method for Fusion of ZY--0C Satellite Images. Remote Sens., 6(), pp Cheng L., Tong L., Li M., et al., 03. Semi-Automati Registration of Airborne and Terrestrial Laser Sanning Data Using Building Corner Mathing with Boundaries as Reliability Chek. Remote Sens., 5(), pp Choi F., and Lee I., 0. A UAV-based lose-range rapid aerial monitoring system for emergeny responses. In: The International Arhives of the Photogrammetry, Remote Sensing and Spatial Information Sienes, XXXVIII-/C, pp Colomina I., and Molina P., 04. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm., 9, pp Gonalves J. A., and Henriques R., 05. UAV photogrammetry for topographi monitoring of oastal areas. ISPRS J. Photogramm., 04, pp. 0-. Forssen P. E., and Lowe D. G., 007. Shape desriptors for maximally stable extremal regions. In Proeedings of IEEE International Conferene on Computer Vision, pp. -8. Gruen A., and Aka D., 005. Least squares 3D surfae and urve mathing. ISPRS J. Photogramm., 59(3), pp Gruen A., 0. Development and Status of Image Mathing in Photogrammetry. Photogramm. Re., 7(37), pp Harris C., and Stephens M., 988. A ombined orner and edge detetor. In Proeedings of the 4th Alvey Vision Conferene. pp Hartley R., and Zisserman A., 000. Multiple View Geometry in Computer Vision, Cambridge university press. doi:0.594/isprsannals-iii

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