scholarly journals Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

2019 ◽  
Vol 11 (6) ◽  
pp. 658 ◽  
Author(s):  
James Shepherd ◽  
Pete Bunting ◽  
John Dymond
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Ground Cover ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Alos Palsar ◽  
Mapping Unit ◽  
Minimum Mapping Unit ◽  
Gis Software

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

scholarly journals Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 397
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Sustainable Management ◽  
Large Scale ◽  
Spectral Signature ◽  
Accurate Information ◽  
Data Interoperability ◽  
Critical Issues ◽  
Management Context ◽  
Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

scholarly journals Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

2020 ◽  
Vol 12 (12) ◽  
pp. 2013
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Alexandros Karagaitanakis ◽  
Apostolos Papakonstantinou ◽  
Manuel Arias Ballesteros
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Experiments ◽  
Spectral Unmixing ◽  
Sea Surface ◽  
Spectral Signature ◽  
Unmanned Aerial Systems ◽  
Real Environment ◽  
Promising Tool ◽  
Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

scholarly journals Remote Sensing by using Unsupervised Algorithm

2021 ◽  
Author(s):  
Afshan Saleem
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Hyperspectral Image ◽  
Relevant Information ◽  
Spectral Signature ◽  
Sensing Applications ◽  
Hyper Spectral ◽  
Wide Range ◽  
Remote Sensing Applications

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

scholarly journals Effects of Location and Season on Seaweed Spectral Signatures

2021 ◽  
Vol 9 ◽  
Author(s):  
Sadhvi Selvaraj ◽  
Bradley S. Case ◽  
W. Lindsey White
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Plant Diseases ◽  
Spectral Signature ◽  
Seasonal Effect ◽  
Spectral Variation ◽  
Seaweed Species ◽  
The Impact

Remote sensing is an effective tool for applications such as discriminating plant species, detecting plant diseases or drought, and mapping aquatic vegetation such as seagrasses and seaweeds. Each plant species has a unique spectral reflectance which can be used with remote sensing to map them. However, variations in season, illumination, phenological stages, turbidity or location may affect the spectral reflectance. The aim of this study is to understand the spatial and seasonal effect on two commonly found New Zealand native seaweed species, Ecklonia radiata (C. Agardh) J. Agardh. and Carpophyllum maschalocarpum (Turner) Grev. We collected hyperspectral data (using ASD Handheld2 Field spectrometer with wavelength range 325–1,075 nm) of the seaweed species from four locations across four seasons and used mixed effects modelling to determine the model that best described the spectral data of each seaweed species. The results showed some seasonal pattern across the four locations. In general, summer has an effect on both the species in all four locations; likely due to the higher rates of photosynthesis. However, location did not effect the spectral signature of either species in winter. This study shows the potential for analysis of other micro-and macro-environment factors of different species and provides an understanding of the degree of natural spectral variation in seaweeds enabling further assessment of the impact of anthropogenic activities and changing environment on their spectral characteristics and health. It also identifies a general trend for best season to collect data for better classification accuracy across larger areas.

A Review on Segmentation Techniques in Large-Scale Remote Sensing Images

2018 ◽  
Vol 4 (4) ◽  
pp. 7
Author(s):  
Rakesh Tripathi ◽  
Neelesh Gupta
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Segmentation ◽  
Large Scale ◽  
Remote Sensing Images ◽  
Processing Step ◽  
Multiple Regions ◽  
Important Processing ◽  
Basic Image ◽  
Computer Vision Applications

Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.

scholarly journals Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4877
Author(s):  
Stéphane Mermoz ◽  
Alexandre Bouvet ◽  
Thierry Koleck ◽  
Marie Ballère ◽  
Thuy Le Toan
Keyword(s):  
Protected Areas ◽  
Large Scale ◽  
Forest Disturbance ◽  
Forest Loss ◽  
False Alarms ◽  
Forest Disturbances ◽  
Mapping Unit ◽  
Minimum Mapping Unit ◽  
Conservation Of Natural Resources ◽  
Do So

In this study, we demonstrate the ability of a new operational system to detect forest loss at a large scale accurately and in a timely manner. We produced forest loss maps every week over Vietnam, Cambodia, and Laos (>750,000 km2 in total) using Sentinel-1 data. To do so, we used the forest loss detection method based on shadow detection. The main advantage of this method is the ability to avoid false alarms, which is relevant in Southeast Asia where the areas of forest disturbance may be very small and scattered and detection is used for alert purposes. The estimated user accuracy of the forest loss map was 0.95 for forest disturbances and 0.99 for intact forest, and the estimated producer’s accuracy was 0.90 for forest disturbances and 0.99 for intact forest, with a minimum mapping unit of 0.1 ha. This represents an important step forward compared to the values achieved by previous studies. We also found that approximately half of forest disturbances in Cambodia from 2018 to 2020 occurred in protected areas, which emphasizes the lack of efficiency in the protection and conservation of natural resources in protected areas. On an annual basis, the forest loss areas detected using our method are found to be similar to the estimations from Global Forest Watch. These results highlight the fact that this method provides not only quick alerts but also reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale.

scholarly journals Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images

2019 ◽  
Vol 9 (11) ◽  
pp. 2389 ◽  
Author(s):  
Chengquan Zhou ◽  
Hongbao Ye ◽  
Zhifu Xu ◽  
Jun Hu ◽  
Xiaoyan Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Learning Algorithm ◽  
Low Cost ◽  
Remote Sensing Images ◽  
Field Phenotyping ◽  
Varying Environments ◽  
Radiation Conditions

Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as Qseg, Sr, Es and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops.

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