scholarly journals Automated detection of rock glaciers using deep learning and object-based image analysis

2020 ◽  
Vol 250 ◽  
pp. 112033
Author(s):  
Benjamin Aubrey Robson ◽  
Tobias Bolch ◽  
Shelley MacDonell ◽  
Daniel Hölbling ◽  
Philipp Rastner ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Automated Detection ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Rock Glaciers

Use of Convolution Neural Networks and Object Based Image Analysis for Automated Rock Glacier Mapping

2020 ◽  
Author(s):  
Benjamin Aubrey Robson ◽  
Tobias Bolch ◽  
Shelley MacDonell ◽  
Daniel Hölbling ◽  
Philip Rastner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Resolution ◽  
Rock Glacier ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Rock Glaciers ◽  
The Creation

<p>Rock glaciers are an important, but often overlooked, component of the cryosphere and are one of the few visible manifestations of permafrost. In certain parts of the world, rock glaciers can contribute up to 30% of catchment streamflow. Remote sensing has permitted the creation of rock glacier inventories for large regions, however, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation of remote sensing data which is both time consuming and subjective. Here, we present a method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 imagery, Sentinel-1 interferometric coherence, and a Digital Elevation Model. CNNs work by identifying recurring patterns and textures and produce a heatmap where each pixel indicates the probability that it belongs to a rock glacier or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics and convert the raw probability heatmap generated by the deeo learning into rock glacier polygons. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment on the Tibetan Plateau. In total, our method mapped 72% of the rock glaciers across both catchments, although many of the individual rock glacier polygons contained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user’s accuracy to be moderate (64-69%) even if the producer’s accuracy was higher (75%). We repeated our method on very-high resolution Pléiades satellite imagery (resampled to 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference the image resolution makes. We found that working at a higher spatial resolution has little influence on the user’s accuracy (an increase of 3%) yet as smaller landforms were mapped, the producer’s accuracy rose by 13% to 88%. By running all the processing within an object-based analysis it was possible to both generate the deep learning heatmap and automate some of the post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning offers a feasible method for automated mapping of rock glaciers over large regional scales.</p>

Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery

2020 ◽  
Vol 41 (9) ◽  
pp. 3446-3479 ◽  
Author(s):  
Huasheng Huang ◽  
Yubin Lan ◽  
Aqing Yang ◽  
Yali Zhang ◽  
Sheng Wen ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Object Based Image Analysis ◽  
Weed Mapping ◽  
Object Based

scholarly journals ROCKY SHORELINE EXTRACTION USING A DEEP LEARNING MODEL AND OBJECT-BASED IMAGE ANALYSIS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 23-29
Author(s):  
S. Bengoufa ◽  
S. Niculescu ◽  
M. K. Mihoubi ◽  
R. Belkessa ◽  
K. Abbad
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Essential Feature ◽  
Integrated Approach ◽  
Learning Model ◽  
Future Evolution ◽  
Object Based Image Analysis ◽  
Main Challenge ◽  
Object Based ◽  
Deep Learning Model

Abstract. In the context of the increasing anthropogenic influence on the coastal areas that are subject to high climate variability, the main challenge is to understand its current dynamics and to predict its future evolution. Therefore, monitoring of the shoreline kinematics is a key factor for the coastal erosion assessment and an essential feature for the sustainable management of these naturally vulnerable areas.This work focuses on the detection and extraction of the shoreline, basing on a specific remote sensing methodology using Very High Resolution (VHR) optical images. Indeed, an integrated approach based on a Deep Learning model, which is the Convolutional Neural Network (CNN) and Object Based Image Analysis (OBIA) has been developed. This study aims to evaluate the methodological contribution of this integrated approach for the (semi)-automatic extraction of the rocky shoreline, for which the botanical indicator has been chosen. Therefore the upper limit of black marine lichen has been detected and extracted as the target shoreline. It is the first indication of a (semi)-automatic detection of such a complex type of shoreline.The classification results derived from the combined CNN model and OBIA methods had achieved a high overall accuracy of 0.94. The extracted shoreline have been compared to a shoreline of reference derived from a traditional method that is a manual digitizing. The distances between the two shorelines has been calculated in order to assess the accuracy of the extraction method. This comparison revealed that 76% of the extracted shoreline lies within 1 m, and 35% lies within 0.5 m of reference one. Therefore, the CNN model integrated to OBIA was successfully shown to be a good method for shoreline extraction and could offer an immediate insight regarding rocky shoreline position, providing an alternative to its monitoring.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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