Palm Trees Counting in Remote Sensing Imagery Using Regression Convolutional Neural Network

Djerriri, Khelifa; Ghabi, M.; Karoui, Moussa Sofiane; Adjoudj, Reda

doi:10.1109/igarss.2018.8519188

“…Since the F1-measure uses both the precision and recall values to compute the test results, it can be assumed that the proposed approach performs better and returns a better balance between true-positive predicted and true-positive rates concerning the identification of palm-trees. Nonetheless, the results are consistent with recent literature where object detection applications were applied for the identification of single tree-species 6,7,56,57 ; but performed in the non-RGB image domain. The low precision values for the bounding box method may be explained by a high density of objects (i.e., M. flexuosa palm-trees).…”

Section: Comparative Results Between Object Detection Methodssupporting

confidence: 90%

“…For the identification of citrus-tree, a CNN method was able to provide 96.2% accuracy 13 , and in oil palm-tree detection, a deep neural network implementation returned an accuracy of 96.0% (Li et al, 2019). One different kind of palm trees than the ones evaluated in our dataset was investigated with a modification of the AlexNet CNN architecture and returned high prediction values (R² = 0.99, with the relative error between 2.6% to 9.2%) 56 . A study 7 achieved an accuracy higher than 90% to detect single tree-species using the RetinaNet and RGB images.…”

Section: Discussionmentioning

confidence: 96%

Mauritia Flexuosa Palm-Trees Airborne Mapping with Deep Convolutional Neural Network

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Osco

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Arruda

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et al. 2021

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Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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“…Since the F1-measure uses both the precision and recall values to compute the test results, it can be assumed that the proposed approach performs better and returns a better balance between true-positive predicted and true-positive rates concerning the identification of palm-trees. Nonetheless, the results are consistent with recent literature where object detection applications were applied for the identification of single tree-species (8,9,68,70); but performed in the non-RGB image domain. The low precision values for the bounding box method may be explained by a high density of objects (i.e., M. flexuosa palm-trees).…”

Section: P R E P R I N Tsupporting

confidence: 90%

“…For the identification of citrus-tree, a CNN method was able to provide 96.2% accuracy (16), and in oil palm-tree detection, a deep neural network implementation returned an accuracy of 96.0% . One different kind of palm trees than the ones evaluated in our dataset was investigated with a modification of the AlexNet CNN architecture and returned high prediction values (R² = 0.99, with the relative error between 2.6% to 9.2%) (70). A study (9) achieved an accuracy higher than 90% to detect single tree-species using the RetinaNet and RGB images.…”

Section: Discussionmentioning

confidence: 98%

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

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MdSd²,

Deg³

et al. 2021

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Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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“…Approaches that consider both spectral and spatial information in their model can improve estimates significantly (Zhang et al, 2017). This has been the most common strategy when dealing with vegetation analysis and deep networks in the last few years (Djerriri et al, 2018;Li et al, 2017;Csillik et al, 2018;Safonova et al, 2019;Weinstein et al, 2019;Osco et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

Osco

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Nogueira

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Ramos

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et al. 2021

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Accurately mapping farmlands is important for precision agriculture practices. Unmanned Aerial Vehicles (UAV) embedded with multispectral cameras are commonly used to map vegetation in these areas. However, separating plantation fields from the remaining objects in a multispectral scene is a difficult task for most traditional algorithms. In this manner, deep learning methods that perform semantic segmentation could help improve the overall outcome. To the best of our knowledge, in the agricultural context, it is yet unknown the performance of deep networks to semantic segmentation in UAV-based multispectral imagery; especially in arboreous vegetation types like citrus-orchards. Here, we evaluate state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images. For this purpose, we used a multispectral camera that operates at the green (530-570 nm), red (640-680 nm), red-edge (730-740 nm), and also near-infrared (770-810 nm) spectral regions. We evaluated the performance of the five state-of-the-art pixelwise methods: FCN, U-Net, SegNet, DeepLabV3+, and DDCN. Our results indicate that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). We also determined the inference time needed per area, and although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-affected areas. We conclude that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 minutes per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of the plantation fields.

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Palm Trees Counting in Remote Sensing Imagery Using Regression Convolutional Neural Network

Cited by 15 publications

References 2 publications

Mauritia Flexuosa Palm-Trees Airborne Mapping with Deep Convolutional Neural Network

Mauritia Flexuosa Palm-Trees Airborne Mapping with Deep Convolutional Neural Network

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

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