Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

Abstract

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

“…This was followed by PSPNet (ResNet-50 Backbone), DeepLab V3+ (Xception Backbone), U-net (VGG-16 Backbone), and DeepLab V3+ (ResNet-50 Backbone). Numerous studies that have used and compared different deep semantic segmentation architectures for tree, crop, and vegetation mapping have reported similar ranges of segmentation metrics [57,70,100,102,117,125]. For instance, five semantic segmentation architectures, including SegNet, U-Net, FC-DenseNet, and DeepLab V3+( based on Xception and MobileNetV2 backbones), were evaluated in Reference [100] for segmenting threatened single tree species from UAV-based images.…”

“…Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108]. Various architectures, such as fully convolutional networks (FCNs), SegNet [109], U-Net [110], and DeepLab V3+ [111], have been used successfully to delineate tree and vegetation species [70,98,100,101,103,105,106,[112][113][114][115][116][117][118][119][120][121][122][123][124], crops [51,57,58,102,125,126], wetlands [107,127], and weeds [61,99] from various remotely sensed data. For instance, Freudenberg et al [128] utilized U-Net architecture to detect oil and coconut palms from WorldView 2, 3 satellite images.…”

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

“…The author argued that this method performed better using multiscale textures than singlescale textures, and concluded that biomass estimation did not significantly improve using hyperspectral data, showing that LSSVM regression with proper multiscale textural features was an optimal and simpler alternative to hyperspectral-based methods. Bhatnagar et al (2020) [39] also compared several ML models, but included DL and segmentation algorithms to map raised bog vegetation communities using RGB and TFs. Based on these performances, RF with graph cut as segmentation technique was selected as the best ML classifier.…”

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

“…Deep learning approaches have shown promising results in many scientific applications [67,68] but do not always guarantee better outcomes [69,70]. Therefore, we compared the performance of the proposed DL model with a RF regression model as a baseline, since RF showed reasonable results and is popular in various machine learning applications [71].…”