High temporal frequency vehicle counting from low-resolution satellite images
Frequent object counting at a specific location (FOC@Loc) is becoming a newly emerging but highly demanded task since the evolution of human activities can provide crucial statistics for social and economic development. Due to the unique requirement of both high temporal frequency for frequent obser...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172855 |
| _version_ | 1811692191188254720 |
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| author | Liao, Liang Xiao, Jing Yang, Yan Ma, Xujie Wang, Zheng Satoh, Shin'ichi |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liao, Liang Xiao, Jing Yang, Yan Ma, Xujie Wang, Zheng Satoh, Shin'ichi |
| author_sort | Liao, Liang |
| collection | NTU |
| description | Frequent object counting at a specific location (FOC@Loc) is becoming a newly emerging but highly demanded task since the evolution of human activities can provide crucial statistics for social and economic development. Due to the unique requirement of both high temporal frequency for frequent observations and high spatial resolution for object counting, this article aims to propose a novel framework for FOC@Loc to take advantage of both high definitions of high-resolution (HR) image and continuous low-cost low-resolution (LR) image at the same location. To compensate for the low ground sample distance (GSD) in LR images, some prior knowledge about the fixed location is extracted from HR images, including (1) the short-term spatial consistency: provide exact feature-wise and pixel-wise guidance from HR image to the LR image on the same date, learning to predict vehicle area for each LR image; (2) the long-term location consistency: provide the prior parking density of the study location from the sparse HR image sequence, turning the vehicle area into the counting number of each LR image. The final results indicate that this new method obtains highly consistent counting results with the manual annotations, proofing that the HR image guidance information can promote the utilization of LR images for object counting and further analysis. The proposed dataset and methodology have the potential to boost the applications of extensive and inexpensive LR satellite images. |
| first_indexed | 2024-10-01T06:31:51Z |
| format | Journal Article |
| id | ntu-10356/172855 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:31:51Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1728552023-12-27T01:15:23Z High temporal frequency vehicle counting from low-resolution satellite images Liao, Liang Xiao, Jing Yang, Yan Ma, Xujie Wang, Zheng Satoh, Shin'ichi School of Computer Science and Engineering Engineering::Computer science and engineering Vehicle Counting Satellite Images Frequent object counting at a specific location (FOC@Loc) is becoming a newly emerging but highly demanded task since the evolution of human activities can provide crucial statistics for social and economic development. Due to the unique requirement of both high temporal frequency for frequent observations and high spatial resolution for object counting, this article aims to propose a novel framework for FOC@Loc to take advantage of both high definitions of high-resolution (HR) image and continuous low-cost low-resolution (LR) image at the same location. To compensate for the low ground sample distance (GSD) in LR images, some prior knowledge about the fixed location is extracted from HR images, including (1) the short-term spatial consistency: provide exact feature-wise and pixel-wise guidance from HR image to the LR image on the same date, learning to predict vehicle area for each LR image; (2) the long-term location consistency: provide the prior parking density of the study location from the sparse HR image sequence, turning the vehicle area into the counting number of each LR image. The final results indicate that this new method obtains highly consistent counting results with the manual annotations, proofing that the HR image guidance information can promote the utilization of LR images for object counting and further analysis. The proposed dataset and methodology have the potential to boost the applications of extensive and inexpensive LR satellite images. This work was supported in part by the National Natural Science Foundation of China under Grant 62202349, 61825103 and in part by Sumitomo Mitsui DS Asset Management Company, Ltd. 2023-12-27T01:15:23Z 2023-12-27T01:15:23Z 2023 Journal Article Liao, L., Xiao, J., Yang, Y., Ma, X., Wang, Z. & Satoh, S. (2023). High temporal frequency vehicle counting from low-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 45-59. https://dx.doi.org/10.1016/j.isprsjprs.2023.02.006 0924-2716 https://hdl.handle.net/10356/172855 10.1016/j.isprsjprs.2023.02.006 2-s2.0-85149704943 198 45 59 en ISPRS Journal of Photogrammetry and Remote Sensing © 2023 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Vehicle Counting Satellite Images Liao, Liang Xiao, Jing Yang, Yan Ma, Xujie Wang, Zheng Satoh, Shin'ichi High temporal frequency vehicle counting from low-resolution satellite images |
| title | High temporal frequency vehicle counting from low-resolution satellite images |
| title_full | High temporal frequency vehicle counting from low-resolution satellite images |
| title_fullStr | High temporal frequency vehicle counting from low-resolution satellite images |
| title_full_unstemmed | High temporal frequency vehicle counting from low-resolution satellite images |
| title_short | High temporal frequency vehicle counting from low-resolution satellite images |
| title_sort | high temporal frequency vehicle counting from low resolution satellite images |
| topic | Engineering::Computer science and engineering Vehicle Counting Satellite Images |
| url | https://hdl.handle.net/10356/172855 |
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