An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data
Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF dat...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/1/55 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549029554520064 |
|---|---|
| author | Xiao Wang Chunyao Yu Xiaohong Zhang Xue Liu Yinxing Zhang Junyong Fang Qing Xiao |
| author_facet | Xiao Wang Chunyao Yu Xiaohong Zhang Xue Liu Yinxing Zhang Junyong Fang Qing Xiao |
| author_sort | Xiao Wang |
| collection | DOAJ |
| description | Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency. |
| format | Article |
| id | doaj-art-de211f9c66ff43e2b461907af1df3c30 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-de211f9c66ff43e2b461907af1df3c302025-01-10T13:20:05ZengMDPI AGRemote Sensing2072-42922024-12-011715510.3390/rs17010055An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral DataXiao Wang0Chunyao Yu1Xiaohong Zhang2Xue Liu3Yinxing Zhang4Junyong Fang5Qing Xiao6Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaMegatronix (Beijing) Technology Co., Ltd., Beijing 100012, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaLinear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency.https://www.mdpi.com/2072-4292/17/1/55linear variable filterhyperspectral databand registrationUAV |
| spellingShingle | Xiao Wang Chunyao Yu Xiaohong Zhang Xue Liu Yinxing Zhang Junyong Fang Qing Xiao An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data Remote Sensing linear variable filter hyperspectral data band registration UAV |
| title | An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data |
| title_full | An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data |
| title_fullStr | An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data |
| title_full_unstemmed | An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data |
| title_short | An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data |
| title_sort | improved registration method for uav based linear variable filter hyperspectral data |
| topic | linear variable filter hyperspectral data band registration UAV |
| url | https://www.mdpi.com/2072-4292/17/1/55 |
| work_keys_str_mv | AT xiaowang animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT chunyaoyu animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT xiaohongzhang animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT xueliu animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT yinxingzhang animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT junyongfang animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT qingxiao animprovedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT xiaowang improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT chunyaoyu improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT xiaohongzhang improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT xueliu improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT yinxingzhang improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT junyongfang improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata AT qingxiao improvedregistrationmethodforuavbasedlinearvariablefilterhyperspectraldata |