Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe

• URL: http://arxiv.org/abs/2411.12897v1
• Date: Tue, 19 Nov 2024 22:25:26 GMT
• Title: Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe
• Authors: Colverd Grace, Schade Laura, Takami Jumpei, Bot Karol, Gallego Joseph,
• Abstract summary: Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. In this study, we employed TomoSense, a 3D tomographic dataset, which utilizes a stack of single-look complex (SLC) images.
• Score: 0.0
• License:
• Abstract: Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. Over the past four decades, remote sensing technologies have been extensively utilized for tree species classification, with Synthetic Aperture Radar (SAR) emerging as a key technique. In this study, we employed TomoSense, a 3D tomographic dataset, which utilizes a stack of single-look complex (SLC) images, a byproduct of SAR, captured at different incidence angles to generate a three-dimensional representation of the terrain. Our research focuses on evaluating multiple tabular machine-learning models using the height information derived from the tomographic image intensities to classify eight distinct tree species. The SLC data and tomographic imagery were analyzed across different polarimetric configurations and geosplit configurations. We investigated the impact of these variations on classification accuracy, comparing the performance of various tabular machine-learning models and optimizing them using Bayesian optimization. Additionally, we incorporated a proxy for actual tree height using point cloud data from Light Detection and Ranging (LiDAR) to provide height statistics associated with the model's predictions. This comparison offers insights into the reliability of tomographic data in predicting tree species classification based on height.

Related papers

• An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds [67.96583737413296]
  We propose an enhanced classification method based on adaptive multi-scale fusion for MPCs with long-tailed distributions. In the training set generation stage, a grid-balanced sampling strategy is designed to reliably generate training samples from sparse labeled datasets. In the feature learning stage, a multi-scale feature fusion module is proposed to fuse shallow features of land-covers at different scales.
  arXiv  Detail & Related papers  (2024-12-16T03:21:20Z)
• PCTreeS: 3D Point Cloud Tree Species Classification Using Airborne LiDAR Images [0.0]
  Current knowledge of tree species distribution relies heavily on manual data collection in the field. Recent works show that state-of-the-art deep learning models using Light Detection and Ranging (LiDAR) images enable accurate and scalable classification of tree species in various ecosystems. This paper offers three significant contributions: (1) we apply the deep learning framework for tree classification in tropical savannas; (2) we use Airborne LiDAR images, which have a lower resolution but greater scalability than Terrestrial LiDAR images used in most previous works; and (3) we introduce the approach of directly feeding 3D point cloud images into a vision transformer model (PCTree
  arXiv  Detail & Related papers  (2024-12-06T02:09:52Z)
• Radio Map Prediction from Aerial Images and Application to Coverage Optimization [46.870065000932016]
  We focus on predicting path loss radio maps using convolutional neural networks. We show that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task. We introduce a new model that slightly exceeds the performance of the present state-of-the-art with reduced complexity.
  arXiv  Detail & Related papers  (2024-10-07T09:19:20Z)
• OAM-TCD: A globally diverse dataset of high-resolution tree cover maps [8.336960607169175]
  We present a novel open-access dataset for individual tree crown delineation (TCD) in high-resolution aerial imagery sourced from OpenMap (OAM) Our dataset, OAM-TCD, comprises 5072 2048x2048px images at 10 cm/px resolution with associated human-labeled instance masks for over 280k individual and 56k groups of trees. Using our dataset, we train reference instance and semantic segmentation models that compare favorably to existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-07-16T14:11:29Z)
• Learning from Synthetic Data for Visual Grounding [55.21937116752679]
  We show that SynGround can improve the localization capabilities of off-the-shelf vision-and-language models. Data generated with SynGround improves the pointing game accuracy of a pretrained ALBEF and BLIP models by 4.81% and 17.11% absolute percentage points, respectively.
  arXiv  Detail & Related papers  (2024-03-20T17:59:43Z)
• Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning [1.6889377382676625]
  We release a large dataset of individual shrub delineations on freely available satellite imagery. We use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data.
  arXiv  Detail & Related papers  (2024-01-31T16:44:20Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• Classification of Single Tree Decay Stages from Combined Airborne LiDAR Data and CIR Imagery [1.4589991363650008]
  This study, for the first time, automatically categorizing individual trees (Norway spruce) into five decay stages. Three different Machine Learning methods - 3D point cloud-based deep learning (KPConv), Convolutional Neural Network (CNN), and Random Forest (RF) All models achieved promising results, reaching overall accuracy (OA) of up to 88.8%, 88.4% and 85.9% for KPConv, CNN and RF, respectively.
  arXiv  Detail & Related papers  (2023-01-04T22:20:16Z)
• Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images [5.185018253122575]
  Individual tree detection and crown delineation (ITDD) are critical in forest inventory management. We propose a ITDD method using the orthophoto and digital surface model (DSM) derived from the multi-view satellite data. Experiments against manually marked tree plots on three representative regions have demonstrated promising results.
  arXiv  Detail & Related papers  (2021-07-01T16:28:43Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.