Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case). First, conventional methods are used to locate local maxima in the CHM and generate an initial map of trees. Second, a graph is built from the LiDAR point cloud, fused with the hyperspectral data. For computational efficiency, the feature space of hyperspectral imagery is reduced using robust PCA. Third, a multi-class normalised cut is applied to the graph, using the initial map of trees to constrain the number of clusters and their locations. Finally, recursive normalised cut is used to subdivide, if necessary, each of the clusters identified by the initial analysis. We call this approach Multiclass Cut followed by Recursive Cut (MCRC). The effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK. The performance of MCRC was usually superior to that of other delineation methods, and was further improved by including high-resolution optical imagery. Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual crown characteristics to be measured. By making full use of the data available, graph cut has the potential to considerably improve the accuracy of tree delineation.
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests. This paper addresses the application of non-parametric image registration techniques to precisely align images obtained from multimodal imaging, which is critical for the successful identification of individual trees using object recognition approaches. Non-parametric image registration, in particular the technique of optimizing one objective function containing data fidelity and regularization terms, provides flexible algorithms for image registration. Using a sur…
Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large amount of spectral information and the presence of redundant and irrelevant bands. Although great progresses have been made on classification techniques, few studies have been done to provide practical guidelines to determine the appropriate classifier for HSI. In this paper, we investigate the performance of four supervised learning algorithms, na…
Local spectral similarity (LSS) algorithm has been developed for detecting homogeneous areas and edges in hyperspectral images (HSIs). The proposed algorithm transforms the 3-D data cube (within a spatial window) into a spectral similarity matrix by calculating the vector-similarity between the center pixel-spectrum and the neighborhood spectra. The final edge intensity is derived upon order statistics of the similarity matrix or spatial convolution of the similarity matrix with the spatial kernels. The LSS algorithm facilitates simultaneous use of spectral-spatial information for the edge det…
Advanced Hyperspectral Data Analysis Software (AVHYAS) plugin is a python3 based quantum GIS (QGIS) plugin designed to process and analyse hyperspectral (Hx) images. It is developed to guarantee full usage of present and future Hx airborne or spaceborne sensors and provides access to advanced algorithms for Hx data processing. The software is freely available and offers a range of basic and advanced tools such as atmospheric correction (for airborne AVIRISNG image), standard processing tools as well as powerful machine learning and Deep Learning interfaces for Hx data analysis.
Huawei Helsinki Research Center, Finland（华为赫尔辛基研究中心（芬兰））；Huawei Technologies, China（华为技术有限公司（中国））
Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper pres…
University of Padova（帕多瓦大学）；Fondazione Edmund Mach（埃德蒙·马赫基金会）；Provincia Autonoma di Trento（特伦托自治省）
This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machin…
Semantic segmentation of forest stands using deep learning
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences（生命科学大学环境科学与自然资源管理学院）；Faculty of Science and Technology, Norwegian University of Life Sciences（生命科学大学科学与技术学院）
Computational Geosciences Inc.（计算地球科学公司）；University of British Columbia（不列颠哥伦比亚大学）
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface characterization. Recently developed fully reversible networks can mostly avoid memory limitations by virtue of having a low and fixed memory requirement for storing network states, as opposed to the typical linear memory growth with depth. Fully reversible networks enable the training of deep neural networks that take in entire data volumes, and create semantic segmentations in one go. This approach avoids the need to …
From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；University of Lodz（卢布林大学）；University of Warsaw, Faculty of Geography and Regional Studies, Department of Geoinformatics, Cartography and Remote Sensing（华沙大学地理与区域研究学院，地理信息学、制图与遥感系）
Prospects for Mitigating Spectral Variability in Tropical Species Classification Using Self-Supervised Learning
AMAP；Univ Montpellier（蒙彼利埃大学）；CIRAD（法国农业发展研究国际合作中心）；CNRS（法国国家科学研究中心）；INRAE（法国国家农业、食品与环境研究院）；IRD（法国发展研究院）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化研究所）；Grenoble INP（格勒诺布尔理工学院）；LJK（约瑟夫·傅里叶实验室）
Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF)（德累斯顿-罗斯托克亥姆霍兹中心（HZDR）、弗里贝格资源技术亥姆霍兹研究所（HIF））；Remote Sensing and Geoinformatics, Freie Universität Berlin（柏林自由大学遥感与地理信息学系）；Institute of Geomatics, BOKU University（博科尼大学测绘学院）；Faculty of Electrical and Computer Engineering, University of Iceland（爱沙尼亚大学电气与计算机工程学院）
