Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/6/12/12037 |
| _version_ | 1828883968891551744 |
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| author | Calvin Hung Zhe Xu Salah Sukkarieh |
| author_facet | Calvin Hung Zhe Xu Salah Sukkarieh |
| author_sort | Calvin Hung |
| collection | DOAJ |
| description | The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%. |
| first_indexed | 2024-12-13T10:59:01Z |
| format | Article |
| id | doaj.art-d2452b880d764cb5a2e6f0d2b444d0ea |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:59:01Z |
| publishDate | 2014-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d2452b880d764cb5a2e6f0d2b444d0ea2022-12-21T23:49:20ZengMDPI AGRemote Sensing2072-42922014-12-01612120371205410.3390/rs61212037rs61212037Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAVCalvin Hung0Zhe Xu1Salah Sukkarieh2The Australian Centre for Field Robotics, University of Sydney, The Rose Street Building J04, Sydney, NSW 2006, AustraliaThe Australian Centre for Field Robotics, University of Sydney, The Rose Street Building J04, Sydney, NSW 2006, AustraliaThe Australian Centre for Field Robotics, University of Sydney, The Rose Street Building J04, Sydney, NSW 2006, AustraliaThe development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%.http://www.mdpi.com/2072-4292/6/12/12037weed classificationUAV remote sensingserrated tussocktropical soda applewater hyacinth |
| spellingShingle | Calvin Hung Zhe Xu Salah Sukkarieh Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV Remote Sensing weed classification UAV remote sensing serrated tussock tropical soda apple water hyacinth |
| title | Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV |
| title_full | Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV |
| title_fullStr | Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV |
| title_full_unstemmed | Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV |
| title_short | Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV |
| title_sort | feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a uav |
| topic | weed classification UAV remote sensing serrated tussock tropical soda apple water hyacinth |
| url | http://www.mdpi.com/2072-4292/6/12/12037 |
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