Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Co-Authors

Journals

Current Science

Year

2022

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Vaidya, Himadri

Air pollution tolerance index of selected roadside plant species in Aizawl, Mizoram, India

Abstract Views :169 | PDF Views:95

Authors

B. P. Mishra ¹, John Lianngura ¹, Madhurima ¹, O. P. Tripathi ¹, Himadri Vaidya ²

Affiliations
1 Department of Environmental Science, Mizoram University, Aizawl 796 004, IN
2 Formerly Uttarakhand Technical University, Dehradun 248 007, IN

Source

Current Science, Vol 122, No 11 (2022), Pagination: 1249-1251

Abstract

No Abstract.

Keywords

No Keywords

Full Text

References

Kumari, J. and Deswal, S., Int. J. Emerg. Technol., 2017, 8, 179–184.

Panda, L. R. L., Aggarwal, R. K. and Bhardwaj, R. D., Curr. World Environ., 2018, 13, 55–65.

Fowler, D., Cape, J. N. and Unsworth, M. H., Philos. Trans. R. Soc. London, 1989, 324, 247–265.

Das, M. and Mukherjee, A., Res. J. Pharm. Biol. Chem. Sci., 2018, 9, 54.

Agbaire, P. O. and Esiefarienrhe, E., J. Appl. Sci. Environ. Manage., 2009, 13, 11– 14.

Begum, A. and Harikrishna, S., Eur. J. Chem., 2010, 7, 151–156.

Chandan, S. and Sanjat, K., Int. J. Emerg. Res. Manage. Technol., 2015, 4, 37–40.

Chauhan, A., N.Y. Sci. J., 2010, 3, 88– 95.

Gour, P. and Haritash, A. K., Int. J. Environ. Sci., 2016, 6, 980–986.

Dhankhar, R., Mor, V., Lilly, S., Chopra, K. and Khokhar, A., Int. J. Rec. Sci. Res., 2015, 6, 2890–2896.

Gholami, A., Mojiri, A. and Hossein, A., J. Anim. Plant Sci., 2016, 26(2), 475–480.

Gupta, S., Mondal, D. and Datta, J. K., Int. Res. J. Plant Sci., 2011, 2, 99–106.

Gupta, G. P., Kumar, B. and Kulshreshta, U. C., Arab. J. Geosci., 2016, 9, 136.

Horaginamani, S. M., Ravichandran, M. and Madar, I. H., Environ. Conserv. J., 2010, 11(1–2), 95–98.

Jyothi, J. S. and Jaya, D. S., J. Environ. Biol., 2010, 31, 379–386.

Karda, S., Kiran, P., Harshika, K. and Gauri, R., Int. J. Plant Anim. Environ. Sci., 2015, 5(3), 128–133.

Krishnaveni, M., Chandrasekar, R., Amsavalli, L., Madhaiyan, P. and Durairaj, S., Int. J. Pharm. Sci. Rev. Res., 2013, 20, 234–239.

Kuddus, M., Rashmi, K. and Pramod, W. R., J. Environ. Res. Manage., 2011, 2, 042–046.

Lakshmi, P., Suvarna, K., Lalitha, S. and Srinivas, N., Ecoscan, 2008, 2, 203–206.

Lohe, R. N., Tyagi, B., Singh, V., Tyagi, P. K., Khanna, D. R. and Bhutiani, R. A., Global J. Environ. Sci. Manage., 2015, 1, 315–324.

Marimuthu, K. V. and Lavanya, K., Int. J. Pharm. Pharm. Sci., 2014, 6, 320–324.

Meerabai, G., Venkataramana, C. and Rasheed, M., Int. J. Environ. Sci., 2012, 2, 1892–1894.

Ogunrotimi, D. G., Adebola, S. I., Akinpelu, B. A. and Awotoye, O. O., J. Appl. Life Sci. Int., 2017, 10, 1–9.

Panigrahi, T., Das, K. K., Dey, B. S., Mishra, M. and Panda, R. B., J. Appl. Chem., 2012, 1, 519–523.

Radhapriya, P., Navaneetha, Gopalakrishnan, A., Malini, P. and Ramachandran, A., J. Environ. Biol., 2012, 33, 635–641.

Thawale, P. R., Satheesh, S., Wakode, R. R., Singh, S. K., Kumar, S. and Juwarkar, A. A., Environ. Monit. Assess., 2011, 177, 527–535.

Yannawar, V. B. and Bhosle, A. B., J. Appl. Phytotechnol. Environ. Sanit., 2014, 3, 23–28.

Barr, H. D. and Weatherley, P. E., AJBS, 1962, 15, 413–428.

Bajaj, K. L. and Kaur, G., Analyst, 1981, 106, 117–120.

Arnon, D. I. and Copper, Plant Physiol., 1949, 24, 1–15.

Singh, S. K. and Rao, D. N., Symposium on Air Pollution Control, Indian Association for Air Pollution Control, New Delhi, 1983, pp. 218–224.

Joshi, N. and Bora, M., Rep. Option, 2011, 3, 42–47.

Joshi, N., Ambika, J. and Bharati, B., Int. J. Life Sci. Sci. Res., 2016, 2(2), 173–182.

Joshi, P. C. and Chauhan, A., Life Sci. J., 2008, 5, 41–45.

Building detection methods from remotely sensed images

Abstract Views :156 | PDF Views:86

Authors

Naveen Chandra ¹, Himadri Vaidya ²

Affiliations
1 Wadia Institute of Himalayan Geology, 33-GMS Road, Dehradun 248 001, IN
2 Formerly Uttarakhand Technical University, Dehradun 248 007, IN

Source

Current Science, Vol 122, No 11 (2022), Pagination: 1252-1267

Abstract

With the availability of high-resolution satellite imagery, new applications have been developed for solving geospatial issues in urban regions. Building detection from remote sensing images has been an active area of research due to its broad range of applications, including city modelling, map updating and urban monitoring. The manual processing of an image is a time-consuming and laborious task. Therefore, researchers have developed methods that involve less or no human effort. At present, building detection has improved through various automated and semi-automated methods/algorithms/ techniques suggested in various studies. The objective of the present study is to review the efforts of such studies. Here, the building detection methods are categorized into six groups: (i) low-level feature-based methods, (ii) snake models, (iii) graph-based methods, (iv) shadow detection-based methods, (v) cognition-based methods and (vi) deep learning models. We hope that this study will aid the researchers working in this domain.

Keywords

Building detection, classification, geospatial issues, remote sensing images, urban areas.

Full Text

References

Chaudhuri, D., Kushwaha, N. K., Samal, A. and Agarwal, R. C., Automatic building detection from high-resolution satellite images based on morphology and internal gray variance. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2015, 9(5), 1767–1779.

Sirmacek, B. and Unsalan, C., Urban-area and building detection using SIFT keypoints and graph theory. IEEE Trans. Geosci. Remote Sensing, 2009, 47(4), 1156–1167.

Ok, A. O., Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS J. Photogramm. Remote Sensing, 2013, 86, 21–40.

Sirmacek, B. and Unsalan, C., A probabilistic framework to detect buildings in aerial and satellite images. IEEE Trans. Geosci. Remote Sensing, 2010, 49(1), 211–221.

Konstantinidis, D., Stathaki, T., Argyriou, V. and Grammalidis, N., Building detection using enhanced HOG–LBP features and region refinement processes. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2016, 10(3), 888–905.

Cheng, G. and Han, J., A survey on object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sensing, 2016, 117, 11–28.

Liasis, G. and Stavrou, S., Building extraction in satellite images using active contours and colour features. Int. J. Remote Sensing, 2016, 37(5), 1127–1153.

Chandra, N. and Ghosh, J. K., A cognitive method for building detection from high-resolution satellite images. Curr. Sci., 2017, 112(5), 1038–1044.

Chandra, N. and Ghosh, J. K., A cognitive viewpoint on building detection from remotely sensed multispectral images. IETE J. Res., 2018, 64(2), 165–175.

Gavankar, N. L. and Ghosh, S. K., Automatic building footprint extraction from high-resolution satellite image using mathematical morphology. Eur. J. Remote Sensing, 2018, 51(1), 182–193.

Gavankar, N. L. and Ghosh, S. K., Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters. Geocarto Int., 2019, 34(6), 626–643.

Jiang, X., He, Y., Li, G., Liu, Y. and Zhang, X. P., Building damage detection via superpixel-based belief fusion of spaceborne SAR and optical images. IEEE Sens. J., 2019, 20(4), 2008– 2022.

Mayer, H., Automatic object extraction from aerial imagery – a survey focusing on buildings. Comput. Vision Image Understand., 1999, 74(2), 138–149.

Baltsavias, E. P., Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS J. Photogramm. Remote Sensing, 2004, 58(3–4), 129–151.

Brenner, C., Building reconstruction from images and laser scanning. Int. J. Appl. Earth Obs. Geoinf., 2005, 6(3–4), 187– 198.

Gruen, A. and Dan, H., TOBAGO – a topology builder for the automated generation of building models. In Automatic Extraction of Man-made Objects from Aerial and Space Images (II), Birkhauser, Basel, Switzerland, 1997, pp. 149–160.

Gruen, A. and Wang, X., CC-Modeler: a topology generator for 3-D city models. ISPRS J. Photogramm. Remote Sensing, 1998, 53(5), 286–295.

Gulch, E., Müller, H., Labe, T. and Ragia, L., On the performance of semiautomatic building extraction. Int. Arch. Photogramm. Remote Sensing, 1998, 32, 331–338.

Gulch, E., Muller, H. and Labe, T., Integration of automatic processes into semiautomatic building extraction. Int. Arch. Photogramm. Remote Sensing, 1999, 32(3; SECT 2W5), 177–186.

Haala, N., Building Reconstruction by Combining Picture and Height Data, Bavarian Academy of Sciences, 1996, p. 12.

Henricsson, O. and Baltsavias, E., 3-D building reconstruction with ARUBA: a qualitative and quantitative evaluation. In Automatic Extraction of Man-made Objects from Aerial and Space Images (II), Birkhauser, Basel, Switzerland, 1997, pp. 65–76.

Fischer, A., Kolbe, T. H., Lang, F., Cremers, A. B., Forstner, W., Plumer, L. and Steinhage, V., Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D. Comput. Vision Image Understand., 1998 72(2), 185–203.

Baillard, C. and Zisserman, A., Automatic reconstruction of piecewise planar models from multiple views. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999, vol. 2, pp. 559– 565.

Suveg, I. and Vosselman, G., Reconstruction of 3D building models from aerial images and maps. ISPRS J. Photogramm. Remote Sensing, 2004, 58(3–4), 202–224.

Brenner, C. and Haala, N., Rapid acquisition of virtual reality city models from multiple data sources. Int. Arch. Photogramm. Remote Sensing, 1998, 32, 323–330.

Brenner, C., Towards fully automatic generation of city models. Int. Arch. Photogramm. Remote Sensing, 2000, 33(B3/1; PART 3), 84–92.

Vosselman, G., Building reconstruction using planar faces in very high density height data. Int. Arch. Photogramm. Remote Sensing, 1999, 32(3; SECT 2W5), 87–94.

Vosselman, G. and Dijkman, S., 3D building model reconstruction from point clouds and ground plans. Int. Arch. Photogramm. Remote Sensing Spat. Inf. Sci., 2001, 34(3/W4), 37–44.

Rottensteiner, F. and Briese, C., Automatic generation of building models from LIDAR data and the integration of aerial images, ISPRS, Dresden, 2003, vol. XXXIV.

Unsalan, C. and Boyer, K. L., A system to detect houses and residential street networks in multispectral satellite images. Comput. Vision Image Understand., 2005, 98(3), 423–461.

Haala, N. and Kada, M., An update on automatic 3D building reconstruction. ISPRS J. Photogramm. Remote Sensing, 2010, 65(6), 570–580.

Shirowzhan, S., Lim, S., Trinder, J., Li, H. and Sepasgozar, S. M. E., Data mining for recognition of spatial distribution patterns of building heights using airborne lidar data. Adv. Eng. Informat., 2020, 43, 101033.

Shufelt, J. A., Exploiting photogrammetric methods for building extraction in aerial images. Int. Arch. Photogramm. Remote Sensing, 1996, 31(B6), 74–79.

Zhang, Y., Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS J. Photogramm. Remote Sensing, 1999, 54(1), 50–60.

Pesaresi, M. and Benediktsson, J. A., A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sensing, 2001, 39(2), 309–320.

Wang, X. and Li, P., Extraction of urban building damage using spectral, height and corner information from VHR satellite images and airborne LiDAR data. ISPRS J. Photogramm. Remote Sensing, 2020, 159, 322–336.

Shackelford, A. K. and Davis, C. H., A combined fuzzy pixelbased and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sensing, 2003, 41(10), 2354–2363.

Benediktsson, J. A., Pesaresi, M. and Amason, K., Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sensing, 2003, 41(9), 1940–1949.

Inglada, J., Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J. Photogramm. Remote Sensing, 2007, 62(3), 236–248.

Sumer, E. and Turker, M., An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images. Comput., Environ. Urban Syst., 2013, 39, 48–62.

Senaras, C., Ozay, M. and Vural, F. T. Y., Building detection with decision fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6(3), 1295–1304.

Tian, J., Cui, S. and Reinartz, P., Building change detection based on satellite stereo imagery and digital surface models. IEEE Trans. Geosci. Remote Sensing, 2013, 52(1), 406–417.

Zhang, Q., Huang, X. and Zhang, G., A morphological building detection framework for high-resolution optical imagery over urban areas. IEEE Geosci. Remote Sensing Lett., 2016, 13(9), 1388–1392.

Dempster, A. P., Laird, N. M. and Rubin, D. B., Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc.: Series B, 1977, 39(1), 1–22.

Norman, M., Mohd Shafri, H. Z., Idrees, M. O., Mansor, S. and Yusuf, B., Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data. Geocarto Int., 2019, 1–24.

Kass, M., Witkin, A. and Terzopoulos, D., Snakes: active contour models. Int. J. Comput. Vision, 1988, 1(4), 321–331.

Li, C., Liu, J. and Fox, M. D., Segmentation of external force field for automatic initialization and splitting of snakes. Pattern Recogn., 2005, 38(11), 1947–1960.

Chan, T. F. and Vese, L., Active contours without edges. IEEE Trans. Image Process., 2001, 10(2), 266–277.

Chan, T. F., Sandberg, B. Y. and Vese, L. A., Active contours without edges for vector-valued images. J. Vis. Commun. Image Represent., 2000, 11(2), 130–141.

Lie, J., Lysaker, M. and Tai, X. C., A binary level set model and some applications to Mumford-Shah image segmentation. IEEE Trans. Image Process., 2006, 15(5), 1171–1181.

Brox, T. and Weickert, J., Level set based image segmentation with multiple regions. In Joint Pattern Recognition Symposium, Springer, Berlin, Germany, 2004, pp. 415–423.

Chen, L., Zhou, Y., Wang, Y. and Yang, J., GACV: geodesicaided C–V method. Pattern Recogn., 2006, 39(7), 1391–1395.

Caselles, V., Kimmel, R. and Sapiro, G., Geodesic active contours. In Proceedings of IEEE International Conference on Computer Vision, Cambridge, MA, USA, 1995, pp. 694–699.

Caselles, V., Kimmel, R. and Sapiro, G., Geodesic active contours. Int. J. Comput. Vision, 1997, 22(1), 61–79.

Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P. and Tannenbaum, A., A geometric snake model for segmentation of medical imagery. IEEE Trans. Med. Imaging, 1997, 16(2), 199–209.

Siddiqi, K., Lauziere, Y. B., Tannenbaum, A. and Zucker, S. W., Area and length minimizing flows for shape segmentation. IEEE Trans. Image Process., 1998, 7(3), 433–443.

Vasilevskiy, A. and Siddiqi, K., Flux maximizing geometric flows. IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24(12), 1565–1578.

Pi, L., Fan, J. and Shen, C., Color image segmentation for objects of interest with modified geodesic active contour method. J. Math. Imaging Vision, 2007, 27(1), 51–57.

Zheng, Y., Li, G., Sun, X. and Zhou, X., A geometric active contour model without re-initialization for color images. Image Vision Comput., 2009, 27(9), 1411–1417.

Hou, Z. and Han, C., Force field analysis snake: an improved parametric active contour model. Pattern Recogn. Lett., 2005, 26(5), 513–526.

Yan, P. and Kassim, A. A., Segmentation of volumetric MRA images by using capillary active contour. Med. Image Anal., 2006, 10(3), 317–329.

Guo, T. and Yasuoka, Y., Snake-based approach for building extraction from high-resolution satellite images and height data in urban areas. In Proceedings of the 23rd Asian Conference on Remote Sensing, Vienna, Austria, 2002, pp. 25–29.

Mayunga, S. D., Zhang, Y. and Coleman, D. J., Semi-automatic building extraction utilizing Quickbird imagery. In Proceedings of the ISPRS Workshop CMRT, 2005, vol. 13, pp. 1–136.

Peng, J., Zhang, D. and Liu, Y., An improved snake model for building detection from urban aerial images. Pattern Recogn. Lett., 2005, 26(5), 587–595.

Cao, G. and Yang, X., Man‐made object detection in aerial images using multistage level set evolution. Int. J. Remote Sensing, 2007, 28(8), 1747–1757.

Karantzalos, K. and Paragios, N., Recognition-driven two-dimensional competing priors toward automatic and accurate building detection. IEEE Trans. Geosci. Remote Sensing, 2009, 47(1), 133–144.

Ahmadi, S., Zoej, M. V., Ebadi, H., Moghaddam, H. A. and Mohammadzadeh, A., Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. Int. J. Appl. Earth Obs. Geoinf., 2010, 12(3), 150–157.

Krishnamachari, S. and Chellappa, R., Delineating buildings by grouping lines with MRFs. IEEE Trans. Image Process., 1996, 5(1), 164–168.

Kim, T. and Muller, J. P., Development of a graph-based approach for building detection. Image Vision Comput., 1999, 17(1), 3–14.

Croitoru, A. and Doytsher, Y., Monocular right-angle building hypothesis generation in regularized urban areas by pose clustering. Photogramm. Eng. Remote Sensing, 2003, 69(2), 151–169.

Katartzis, A. and Sahli, H., A stochastic framework for the identification of building rooftops using a single remote sensing image. IEEE Trans. Geosci. Remote Sensing, 2007, 46(1), 259–271.

Cui, S., Yan, Q., Reinartz, P. and Mansour, N., Graph search and its application in building extraction from high resolution remote sensing imagery. Search Algorithms and Applications, 2011.

Schindler, K., An overview and comparison of smooth labeling methods for land-cover classification. IEEE Trans. Geosci. Remote Sensing, 2012, 50(11), 4534–4545.

Li, Y., Tan, Y., Li, Y., Qi, S. and Tian, J., Built-up area detection from satellite images using multikernel learning, multifield integrating, and multihypothesis voting. IEEE Geosci. Remote Sensing Lett., 2015, 12(6), 1190–1194.

Smits, P. C. and Annoni, A., Updating land-cover maps by using texture information from very high-resolution space-borne imagery. IEEE Trans. Geosci. Remote Sensing, 1999, 37(3), 1244–1254.

Weizman, L. and Goldberger, J., Urban-area segmentation using visual words. IEEE Geosci. Remote Sensing Lett., 2009, 6(3), 388–392.

Tao, C., Tan, Y., Yu, J. G. and Tian, J., Urban area detection using multiple kernel learning and graph cut. In IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 83–86.

Huertas, A. and Nevatia, R., Detecting buildings in aerial images. Comput. Vision, Graph. Image Process., 1988, 41(2), 131–152.

Irvin, R. B. and McKeown, D. M., Methods for exploiting the relationship between buildings and their shadows in aerial imagery. IEEE Trans. Syst. Man, Cybern., 1989, 19(6), 1564–1575.

Mohan, R. and Nevatia, R., Using perceptual organization to extract 3D structures. IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11(11), 1121–1139.

Liow, Y. T. and Pavlidis, T., Use of shadows for extracting buildings in aerial images. In Structural Pattern Analysis, 1990, pp. 165–180.

Shufelt, J. and McKeown, D. M., Fusion of monocular cues to detect man-made structures in aerial imagery. CVGIP: Image Understand., 1993, 57(3), 307–330.

McGlone, J. C. and Shufelt, J. A., Projective and object space geometry for monocular building extraction (No. CMU-CS-94118). Carnegie-Mellon University of Pittsburgh PA, Department of Computer Science, USA, 1994.

Lin, C. and Nevatia, R., Building detection and description from a single intensity image. Comput. Vision Image Understand., 1998, 72(2), 101–121.

Gevers, T. and Smeulders, A. W., Color-based object recognition. Pattern Recogn., 1999, 32(3), 453–464.

Stassopoulou, A. and Caelli, T., Building detection using Bayesian networks. Int. J. Pattern Recogn. Artif. Intell., 2000, 14(6), 715– 733.

Polidorio, A. M., Flores, F. C., Imai, N. N., Tommaselli, A. M. and Franco, C., Automatic shadow segmentation in aerial color images. In 16th IEEE Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), 2003, pp. 270– 277.

Turker, M. U. S. T. A. F. A. and San, B. T., Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs. Int. J. Remote Sensing, 2004, 25(21), 4701–4714.

Sarabandi, P., Yamazaki, F., Matsuoka, M. and Kiremidjian, A., Shadow detection and radiometric restoration in satellite high resolution images. In IEEE International Geoscience and Remote Sensing Symposium, 2004, vol. 6, pp. 3744–3747.

Peng, J. and Liu, Y. C., Model and context‐driven building extraction in dense urban aerial images. Int. J. Remote Sensing, 2005, 26(7), 1289–1307.

Tsai, V. J., A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Trans. Geosci. Remote Sensing, 2006, 44(6), 1661–1671.

Chung, K. L., Lin, Y. R. and Huang, Y. H., Efficient shadow detection of color aerial images based on successive thresholding scheme. IEEE Trans. Geosci. Remote Sensing, 2008, 47(2), 671– 682.

Akçay, H. G. and Aksoy, S., Building detection using directional spatial constraints. In IEEE International Geoscience and Remote Sensing Symposium, 2010, pp. 1932–1935.

Teke, M., Başeski, E., Ok, A. Ö., Yuksel, B. and Şenaras, Ç., Multispectral false color shadow detection. In ISPRS Conference on Photogrammetric Image Analysis, Springer, Berlin, Germany, 2011, pp. 109–119.

Izadi, M. and Saeedi, P., Three-dimensional polygonal building model estimation from single satellite images. IEEE Trans. Geosci. Remote Sensing, 2011, 50(6), 2254–2272.

Ok, A. O., Senaras, C. and Yuksel, B., Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Trans. Geosci. Remote Sensing, 2012, 51(3), 1701–1717.

Ghaffarian, S., Automatic building detection based on supervised classification using high resolution Google Earth images. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2014, 40(3), 101.

Senaras, C. and Vural, F. T. Y., A self-supervised decision fusion framework for building detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2015, 9(5), 1780–1791.

Manno-Kovacs, A. and Ok, A. O., Building detection from monocular VHR images by integrated urban area knowledge. IEEE Geosci. Remote Sensing Lett., 2015, 12(10), 2140–2144.

Benedek, C., Descombes, X. and Zerubia, J., Building detection in a single remotely sensed image with a point process of rectangles. In 20th IEEE International Conference on Pattern Recognition, 2010, pp. 1417–1420.

Ok, A. O., Automated detection of buildings and roads in urban areas from VHR satellite images. J. Geodesy Geoinf., 2016, 3(1), 29–38.

Liu, W. and Prinet, V., Building detection from high-resolution satellite image using probability model. In Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS’05), 2005, vol. 6, pp. 3888–3891.

Zhang, C., Wang, T., Liu, X. and Zhang, S., Cognitive model based method for earthquake damage assessment from high-resolution satellite images: a study following the WenChuan earthquake. In Sixth IEEE International Conference on Natural Computation, 2010, vol. 4, pp. 2079–2083.

Chandra, N., Sharma, A. and Ghosh, J. K., A cognitive method for object detection from aerial image. In IEEE International Conference on Computing, Communication and Automation (ICCCA), 2016, pp. 327–330.

Chandra, N., Ghosh, J. K. and Sharma, A., A cognitive based approach for building detection from high resolution satellite images. In IEEE International Conference on Advances in Computing, Communication and Automation (ICACCA) (Spring), 2016, pp. 1–5.

Chandra, N., Ghosh, J. K. and Sharma, A., A cognitive framework for road detection from high-resolution satellite images. Geocarto Int., 2019, 34(8), 909–924.

Chandra, N. and Ghosh, J. K., A cognitive perspective on road network extraction from high resolution satellite images. In IEEE Second International Conference on Next Generation Computing Technologies (NGCT), 2016, pp. 772–776.

Das, S., Mirnalinee, T. T. and Varghese, K., Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE Trans. Geosci. Remote sensing, 2011, 49(10), 3906–3931.

Dong, W., Liao, H., Roth, R. E. and Wang, S., Eye tracking to explore the potential of enhanced imagery basemaps in web mapping. Cartograph. J., 2014, 51(4), 313–329.

Sharma, A., Kumar Ghosh, J. and Kolay, S., Fixation data analysis for complex high-resolution satellite images. Geocarto Int., 2019, 1–22.

Sharma, A., Ghosh, J. and Kolay, S., Reference data preparation for complex satellite image segmentation. IET Image Processing, 2019.

Bianchetti, R. A., Considering visual perception and cognition in the analysis of remotely sensed images. In Conference on Spatial Information Theory, 2013.

White, R. A., Coltekin, A. and Hoffman, R. R. (eds), Remote Sensing and Cognition: Human Factors in Image Interpretation, CRC Press, 2018.

Montello, D. R. and Freundschuh, S., Cognition of geographic information. A Research Agenda for Geographic Information Science, 2005, 61–91.

Bianchetti, R. A. and MacEachren, A. M., Cognitive themes emerging from air photo interpretation texts published to 1960. ISPRS Int. J. Geo-Inf., 2015, 4(2), 551–571.

Bianchetti, R. A., Looking back to inform the future: the role of cognition in forest disturbance characterization from remote sensing imagery, Doctoral Thesis, The Pennsylvania State University, 2014.

Barkowsky, T. and Freksa, C., Cognitive requirements on making and interpreting maps. In International Conference on Spatial Information Theory, Springer, Berlin, Germany, 1997, pp. 347–361.

Battersby, S. E., Hodgson, M. E. and Wang, J., Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster. Photogramm. Eng. Remote Sensing, 2012, 78(6), 625–635.

Kang, L., Zou, B., Zhang, Y. and Zhang, L., Building detection based on human visual cognition mechanism using PolSAR images. In IEEE Geoscience and Remote Sensing Symposium, 2014, pp. 2742–2745.

Zou, B., Zhang, Y., Wang, C. and Cheng, Y., Building cognition method based on human images cognition mechanism in high resolution PolSAR images. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 3223–3226.

Zou, B., Xu, X., Zhang, L. and Song, C., High-resolution PolSAR image interpretation based on human image cognition mechanism. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2018, 11(11), 4259–4269.

Ayala, C., Sesma, R., Aranda, C. and Galar, M., A deep learning approach to an enhanced building footprint and road detection in high-resolution satellite imagery. Remote Sensing, 2021, 13(16), 3135.

Ji, S., Wei, S. and Lu, M., Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans. Geosci. Remote Sensing, 2018, 57(1), 574–586.

Vakalopoulou, M., Karantzalos, K., Komodakis, N. and Paragios, N., Building detection in very high resolution multispectral data with deep learning features. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 1873–1876.

Ishii, T., Simo-Serra, E., Iizuka, S., Mochizuki, Y., Sugimoto, A., Ishikawa, H. and Nakamura, R., Detection by classification of buildings in multispectral satellite imagery. In 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 3344–3349.

Miyazaki, H., Kuwata, K., Ohira, W., Guo, Z., Shao, X., Xu, Y. and Shibasaki, R., Development of an automated system for building detection from high-resolution satellite images. In IEEE Fourth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 2016, pp. 245–249.

Xu, Z., Wang, R., Zhang, H., Li, N. and Zhang, L., Building extraction from high-resolution SAR imagery based on deep neural networks. Remote Sensing Lett., 2017, 8(9), 888–896.

Vakalopoulou, M., Bus, N., Karantzalos, K. and Paragios, N., Integrating edge/boundary priors with classification scores for building detection in very high resolution data. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 3309–3312.

Xu, Y., Wu, L., Xie, Z. and Chen, Z., Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sensing, 2018, 10(1), 144.

Prathap, G. and Afanasyev, I., Deep learning approach for building detection in satellite multispectral imagery. In IEEE International Conference on Intelligent Systems (IS), 2018, pp. 461–465.

Hui, J., Du, M., Ye, X., Qin, Q. and Sui, J., Effective building extraction from high-resolution remote sensing images with multitask driven deep neural network. IEEE Geosci. Remote Sensing Lett., 2018, 16(5), 786–790.

Emek, R. A. and Demir, N., Building detection from SAR images using U-Net deep learning method. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2020, 44, 215–218.

Zheng, L., Ai, P. and Wu, Y., Building recognition of UAV remote sensing images by deep learning. In IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 1185–1188.

Chen, M. et al., DRNet: an improved network for building extraction from high resolution remote sensing image. Remote Sensing, 2021, 13(2), 294.

Moghalles, K., Li, H. C., Al-Huda, Z. and Hezzam, E. A., Multitask deep network for semantic segmentation of building in very high resolution imagery. In IEEE International Conference of Technology, Science and Administration (ICTSA), 2021, pp. 1–6.

Benedek, C., Descombes, X. and Zerubia, J., Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics. IEEE Trans. Pattern Anal. Mach. Intell., 2011, 34(1), 33–50.

Mnih, V., Machine Learning for Aerial Image Labeling, Doctor of Philosophes, Graduate Department of Computer Science, University of Toronto, Canada, 2013.

Khalel, A. and El-Saban, M., Automatic pixelwise object labeling for aerial imagery using stacked u-nets, 2018, preprint arXiv: 1803.04953.

Maggiori, E., Tarabalka, Y., Charpiat, G. and Alliez, P., Can semantic labeling methods generalize to any city? The Inria Aerial Image Labeling benchmark. In IEEE International Geoscience and Remote Sensing Symposium, 2017, pp. 3226–3229.

Ji, S., Wei, S. and Lu, M., Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans. Geosci. Remote Sensing, Austria, 2018, 57(1), 574–586.

Thuy, N. T., Object detection from aerial image. Doctoral dissertation, Graz University of Technology, 2009.

Yang, Y. and Newsam, S., Bag-of-visual-words and spatial extensions for land-use classification. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, pp. 270–279.

Zou, Q., Ni, L., Zhang, T. and Wang, Q., Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sensing Lett., 2015, 12(11), 2321–2325.

Cheng, G., Han, J. and Lu, X., Remote sensing image scene classification: Benchmark and state of the art. Proc. IEEE, 2017, 105(10), 1865–1883.

Demir, I. et al., A challenge to parse the earth through satellite images. 2018, preprint arXiv:1805.06561.

Username
Password
Remember me