Background Information Self-Learning Based Hyperspectral Target Detection
Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we propo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3502508 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555568452075520 |
|---|---|
| author | Yufei Tian Jihai Yang Shijun Li Wenning Xu |
| author_facet | Yufei Tian Jihai Yang Shijun Li Wenning Xu |
| author_sort | Yufei Tian |
| collection | DOAJ |
| description | Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images. |
| format | Article |
| id | doaj-art-e1faaf4e402f459aa2c11ca35a5fe57e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e1faaf4e402f459aa2c11ca35a5fe57e2025-02-03T05:47:57ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/35025083502508Background Information Self-Learning Based Hyperspectral Target DetectionYufei Tian0Jihai Yang1Shijun Li2Wenning Xu3Hubei Bosheng Digital Education Service Co., Ltd., Wuhan, ChinaSchool of Computer, Wuhan University, Wuhan, ChinaSchool of Computer, Wuhan University, Wuhan, ChinaChinese Academy of Geological Sciences, Beijing, ChinaHyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.http://dx.doi.org/10.1155/2018/3502508 |
| spellingShingle | Yufei Tian Jihai Yang Shijun Li Wenning Xu Background Information Self-Learning Based Hyperspectral Target Detection Complexity |
| title | Background Information Self-Learning Based Hyperspectral Target Detection |
| title_full | Background Information Self-Learning Based Hyperspectral Target Detection |
| title_fullStr | Background Information Self-Learning Based Hyperspectral Target Detection |
| title_full_unstemmed | Background Information Self-Learning Based Hyperspectral Target Detection |
| title_short | Background Information Self-Learning Based Hyperspectral Target Detection |
| title_sort | background information self learning based hyperspectral target detection |
| url | http://dx.doi.org/10.1155/2018/3502508 |
| work_keys_str_mv | AT yufeitian backgroundinformationselflearningbasedhyperspectraltargetdetection AT jihaiyang backgroundinformationselflearningbasedhyperspectraltargetdetection AT shijunli backgroundinformationselflearningbasedhyperspectraltargetdetection AT wenningxu backgroundinformationselflearningbasedhyperspectraltargetdetection |