An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan

Wang, Jianjun; Zhou, Guisheng; Zhang, Yun; Bussink, Coen; Zhang, Jiahua; Ge, Hao

doi:10.1080/2150704x.2016.1201222

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Given recent advancements in remote sensing and imagery analysis, the detection of individual plant species and their distributions has become a more realistic goal for land managers with training and access to satellite image data and analysis software. In particular, the mixture tuned matched filtering (MTMF) linear unmixing algorithm has proven an effective tool for identifying the presence and abundance of specific land cover types and endmembers [1][2][3][4][5]. In contrast to other forms of spectral unmixing (e.g., multiple endmember spectral mixing analysis), MTMF distinguishes itself by requiring the user to supply only the target spectral signature(s) and not the signatures of background features [2].…”

Section: Background: Mtmfmentioning

confidence: 99%

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

et al. 2018

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Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies.

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Section: Background: Mtmfmentioning

confidence: 99%

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

et al. 2018

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“…Then, they generated a poppy distribution map with an overall accuracy of 73%. In 2016, the same group used an unsupervised mixture-tuned matched filtering (MTMF)-based method to detect poppies, which was more than 10 times faster than their previous method that had similar detection accuracy [10]. This research provided a way to rapidly monitor poppy cultivation over large areas; however, the detection accuracy is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Opium Poppy Detection Using Deep Learning

et al. 2018

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Opium poppies are a major source of traditional drugs, which are not only harmful to physical and mental health, but also threaten the economy and society. Monitoring poppy cultivation in key regions through remote sensing is therefore a crucial task; the location coordinates of poppy parcels represent particularly important information for their eradication by local governments. We propose a new methodology based on deep learning target detection to identify the location of poppy parcels and map their spatial distribution. We first make six training datasets with different band combinations and slide window sizes using two ZiYuan3 (ZY3) remote sensing images and separately train the single shot multibox detector (SSD) model. Then, we choose the best model and test its performance using 225 km2 verification images from Lao People’s Democratic Republic (Lao PDR), which exhibits a precision of 95% for a recall of 85%. The speed of our method is 4.5 km2/s on 1080TI Graphics Processing Unit (GPU). This study is the first attempt to monitor opium poppies with the deep learning method and achieve a high recognition rate. Our method does not require manual feature extraction and provides an alternative way to rapidly obtain the exact location coordinates of opium poppy cultivation patches.

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Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran

2020

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An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan

Cited by 4 publications

References 11 publications

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

Opium Poppy Detection Using Deep Learning

Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran

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