[1] L BENNINGTON ALLISON. Application of multi-spectral remote sensing for crop discrimination in Afghanistan(2008).

[2] S CHUINSIRI, F BLASCO, M F BELLAN et al. A poppy survey using high resolution remote sensing data. International Journal of Remote Sensing, 18, 393-407(1997).

[3] Y C TIAN, B F WU, L ZHANG et al. Opium poppy monitoring with remote sensing in North Myanmar. International Journal of Drug Policy, 22, 278-284(2011).

[4] K JIA, B F WU, Y C TIAN et al. Spectral discrimination of opium poppy using field spectrometry. IEEE Transactions on Geoscience and Remote Sensing, 49, 3414-3422(2011).

[5] J J WANG, Y ZHANG, C BUSSINK. Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand， Afghanistan. Science of the Total Environment, 476/477, 1-6(2014).

[6] J J WANG, G S ZHOU, Y ZHANG et al. 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. Remote Sensing Letters, 7, 945-954(2016).

[7] D M SIMMS, T W WAINE, J C TAYLOR et al. Image segmentation for improved consistency in image-interpretation of opium poppy. International Journal of Remote Sensing, 37, 1243-1256(2016).

[8] D M SIMMS, T W WAINE, J C TAYLOR. Improved estimates of opium cultivation in Afghanistan using imagery-based stratification. International Journal of Remote Sensing, 38, 3785-3799(2017).

[9] S DEMIR, L BAŞAYIĞIT. Determination of opium poppy （papaver somniferum） parcels using high-resolution satellite imagery. Journal of the Indian Society of Remote Sensing, 47, 977-987(2019).

[10] Q HE, Y ZHANG, L LIANG. Identification of poppy by spectral matching classification. Optik, 200, 163445(2020).

[11] Q HE, Y ZHANG, L LIANG. Application of linear spectral mixed pixel decomposition technology in extracting the spatial distribution of illicit opium poppy cultivation. Optik, 271, 170104(2022).

[12] X Y LIU, Y C TIAN, C YUAN et al. Opium poppy detection using deep learning. Remote Sensing, 10, 1886(2018).

[13] J ZHOU, Y C TIAN, C YUAN et al. Improved UAV opium poppy detection using an updated YOLOv3 model. Sensors, 19, 4851(2019).

[14] C S WANG, Q WANG, H R WU et al. Low-altitude remote sensing opium poppy image detection based on modified YOLOv3. Remote Sensing, 13, 2130(2021).

[15] Q WANG, C S WANG, H R WU et al. A two-stage low-altitude remote sensing papaver somniferum image detection system based on YOLOv5s+DenseNet121. Remote Sensing, 14, 1834(2022).

[16] J ZHU, C H ZHANG, C J ZHANG. Papaver somniferum and Papaver rhoeas classification based on visible capsule images using a modified MobileNetV3-small network with transfer learning. Entropy, 25, 447(2023).

[17] Z Q ZHANG, W D XIA, G Q XIE et al. Fast opium poppy detection in Unmanned Aerial Vehicle （UAV） imagery based on deep neural network. Drones, 7, 559(2023).

[18] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[19] V BADRINARAYANAN, A KENDALL, R CIPOLLA. SegNet： A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[20] O RONNEBERGER, P FISCHER, T BROX. U-net： Convolutional networks for biomedical image segmentation, 234-241(2015).

[21] L C Chen, G Papandreou, I Kokkinos et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs, -2015(2015).

[22] L C Chen, G Papandreou, F Schroff et al. Rethinking atrous convolution for semantic image segmentation(2017).

[23] L C CHEN, G PAPANDREOU, I KOKKINOS et al. DeepLab： Semantic image segmentation with deep convolutional nets， atrous convolution， and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[24] L C CHEN, Y K ZHU, G PAPANDREOU et al. Encoder-decoder with atrous separable convolution for semantic image segmentation, 833-851(2018).

[25] H S ZHAO, J P SHI, X J QI et al. Pyramid scene parsing network, 6230-6239(2017).

[26] J FU, J LIU, H J TIAN et al. Dual attention network for scene segmentation, 3141-3149(2019).

[28] P Prapinmongkolkarn, C Thisayakorn, N Kattiyakulwanich et al. Remote sensing applications to land cover classification in Northern Thailand ［Landsat］. Journal of Biological Chemistry, 270, 10828-10832(1980).

[29] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).

[30] M SANDLER, A HOWARD, M L ZHU et al. MobileNetV2： Inverted residuals and linear bottlenecks, 4510-4520(2018).

[31] A G HOWARD, M L ZHU, B CHEN et al. MobileNets： Efficient convolutional neural networks for mobile vision applications. ArXiv(2017).

[32] J HU, L SHEN, S ALBANIE et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[33] A AHMADIS. V-Net： Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation(2016).