Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images

We segment buildings and trees from aerial photographs by using superpixels, and we estimate the tree’s parameters by using a cost function proposed in this paper. A method based on image complexity is proposed to refine superpixels boundaries. In order to classify buildings from ground and classify trees from grass, the salient feature vectors that include colors, Features from Accelerated Segment Test (FAST) corners, and Gabor edges are extracted from refined superpixels. The vectors are used to train the classifier based on Naive Bayes classifier. The trained classifier is used to classify refined superpixels as object or nonobject. The properties of a tree, including its locations and radius, are estimated by minimizing the cost function. The shadow is used to calculate the tree height using sun angle and the time when the image was taken. Our segmentation algorithm is compared with other two state-of-the-art segmentation algorithms, and the tree parameters obtained in this paper are compared to the ground truth data. Experiments show that the proposed method can segment trees and buildings appropriately, yielding higher precision and better recall rates, and the tree parameters are in good agreement with the ground truth data.

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CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-339-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 339-341

Author(s):

R. Kaczynski ◽

A. Rylko

Keyword(s):

Supervised Classification ◽

Local Population ◽

Atmospheric Correction ◽

Ground Truth ◽

Landsat Tm ◽

Aerial Photographs ◽

Radiometric Calibration ◽

Topographic Map ◽

Ground Truth Data ◽

Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

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POST-FIRE FORESTRY RECOVERY MONITORING USING HIGH-RESOLUTION MULTISPECTRAL IMAGERY FROM UNMANNED AERIAL VEHICLES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w8-301-2019 ◽

2019 ◽

Vol XLII-3/W8 ◽

pp. 301-305 ◽

Cited By ~ 1

Author(s):

L. Pádua ◽

T. Adão ◽

N. Guimarães ◽

A. Sousa ◽

E. Peres ◽

...

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicles ◽

Tree Height ◽

Ground Truth ◽

Cost Effective ◽

Forest Disturbances ◽

Ground Truth Data ◽

High Resolution Data ◽

Aerial Vehicles ◽

Fire Scenarios

<p><strong>Abstract.</strong> In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios.</p>

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An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

Remote Sensing ◽

10.3390/rs13224511 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4511

Author(s):

Hui Zhang ◽

Zhixin Qi ◽

Xia Li ◽

Yimin Chen ◽

Xianwei Wang ◽

...

Keyword(s):

Urban Area ◽

Urban Areas ◽

Ground Truth ◽

Detection Accuracy ◽

Urban Flooding ◽

Polarimetric Sar ◽

Sar Images ◽

Ground Truth Data ◽

Optical Images ◽

Coefficient Increase

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

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Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

Remote Sensing ◽

10.3390/rs13173509 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3509

Author(s):

Andrea De Giorgi ◽

David Solarna ◽

Gabriele Moser ◽

Deodato Tapete ◽

Francesca Cigna ◽

...

Keyword(s):

Change Detection ◽

Optical Sensors ◽

Spatial Information ◽

Contextual Information ◽

Temporal Correlation ◽

Ground Truth ◽

Recovery Phase ◽

Geometrical Structures ◽

Ground Truth Data ◽

Optical Images

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019.

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Forgotten Nazi Forced Labour Camps: Arbeitslager Riese (Lower Silesia, SE Poland) and the Use of Archival Aerial Photography and Contemporary LiDAR and Ground Truth Data to Identify and Delineate Camp Areas

Remote Sensing ◽

10.3390/rs12111802 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1802

Author(s):

Aleksander Kamola ◽

Sebastian Różycki ◽

Paweł Bylina ◽

Piotr Lewandowski ◽

Adam Burakowski

Keyword(s):

Local Community ◽

Ground Truth ◽

Concentration Camps ◽

Aerial Photographs ◽

Lidar Data ◽

Ground Truth Data ◽

Field Inspection ◽

Se Poland ◽

National Archives ◽

Forced Labour

The “Riese” project was a huge construction project initiated by German Nazi authorities, which was located in the northeast of the Sowie Mountains (Ger. Eulengebirge) in southwestern Poland. Construction of the “Riese” complex took place in 1943–1945 but was left unfinished. Due to the lack of reliable sources, the exact intended function of the Riese complex is still unknown. The construction was carried out by prisoners, mostly Jews, from the main nearby concentration camps, KL Gross-Rosen and KL Auschwitz-Birkenau. Thanks to the discovery in the National Archives (NARA, USA) of a valuable series of German aerial photographs taken in February 1945, insight into the location of labour camps was obtained. These photographs, combined with LiDAR data from the Head Office of Geodesy and Cartography (Warsaw, Poland), allowed for the effective identification and field inspection of the camps’ remains. The location and delimitation of the selected labour camps were confirmed by an analysis of the 1945 aerial photograph combined with LiDAR data. These results were supported by field inspection as well as archival testimonies of witnesses. The field inspection of the construction remains indicated intentionally faulty construction works, which deliberately reduced the durability of the buildings and made them easy to demolish. The authors believe that it is urgent to continue the research and share the results with both the scientific community and the local community. The authors also want to emphasize that this less-known aspect of Holocaust history is gradually disappearing in social and institutional memory and is losing to the commercial mythologization of the Riese object.

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Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Remote Sensing ◽

10.3390/rs11202351 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2351 ◽

Cited By ~ 3

Author(s):

Yusupujiang Aimaiti ◽

Wen Liu ◽

Fumio Yamazaki ◽

Yoshihisa Maruyama

Keyword(s):

Synthetic Aperture Radar ◽

Ground Truth ◽

Weather Conditions ◽

Synthetic Aperture ◽

Ground Truth Data ◽

Active Sensor ◽

Optical Images ◽

Comparison Results ◽

Landslide Mapping ◽

Aperture Radar

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

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Validation of Two MODIS Aerosols Algorithms with SKYNET and Prospects for Future Climate Satellites Such as the GCOM-C/SGLI

Advances in Meteorology ◽

10.1155/2013/508064 ◽

2013 ◽

Vol 2013 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Jules R. Dim ◽

Tamio Takamura ◽

Akiko Higurashi ◽

Pradeep Kathri ◽

Nobuyuki Kikuchi ◽

...

Keyword(s):

Refractive Index ◽

Global Change ◽

Optical Thickness ◽

Validation Study ◽

Ground Truth ◽

Future Climate ◽

The Other ◽

Asymmetry Factor ◽

Ground Truth Data ◽

Good Agreement

Potential improvements of aerosols algorithms for future climate-oriented satellites such as the coming Global Change Observation Mission Climate/Second generation Global Imager (GCOM-C/SGLI) are discussed based on a validation study of three years’ (2008–2010) daily aerosols properties, that is, the aerosol optical thickness (AOT) and the Ångström exponent (AE) retrieved from two MODIS algorithms. The ground-truth data used for this validation study are aerosols measurements from 3 SKYNET ground sites. The results obtained show a good agreement between the ground-truth data AOT and that of one of the satellites’ algorithms, then a systematic overestimation (around 0.2) by the other satellites’ algorithm. The examination of the AE shows a clear underestimation (by around 0.2–0.3) by both satellites’ algorithms. The uncertainties explaining these ground-satellites’ algorithms discrepancies are examined: the cloud contamination affects differently the aerosols properties (AOT and AE) of both satellites’ algorithms due to the retrieval scale differences between these algorithms. The deviation of the real part of the refractive index values assumed by the satellites’ algorithms from that of the ground tends to decrease the accuracy of the AOT of both satellites’ algorithms. The asymmetry factor (AF) of the ground tends to increase the AE ground-satellites discrepancies as well.

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Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images

International journal of electrical and computer engineering systems ◽

10.32985/ijeces.11.2.1 ◽

2020 ◽

Vol 11 (2) ◽

pp. 53-66

Author(s):

Ivan Aleksi ◽

Tomislav Matić ◽

Benjamin Lehmann ◽

Dieter Kraus

Keyword(s):

Image Segmentation ◽

Success Rate ◽

Mobile Robotics ◽

Ground Truth ◽

Ground Truth Data ◽

Side Scan Sonar ◽

Search Image ◽

Sonar Image ◽

Sonar Images ◽

The Cost

This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentation (RASIS) algorithm. RASIS is applied on Mine-Like Objects (MLO) in sonar images, where an object is defined by highlight and shadow regions, i.e. regions of high and low pixel intensities in a side-scan sonar image. RASIS uses a modified A*-Search method, which is usually used in mobile robotics for finding the shortest path where the environment map is predefined, and the start/goal locations are known. RASIS algorithm represents the image segmentation problem as a path-finding problem. Main modification concerning the original A*-Search is in the cost function that takes pixel intensities and contour curvature in order to navigate the 2D segmentation contour. The proposed method is implemented in Matlab and tested on real MLO images. MLO image dataset consist of 70 MLO images with manta mine present, and 70 MLO images with cylinder mine present. Segmentation success rate is obtained by comparing the ground truth data given by the human technician who is detecting MLOs. Measured overall success rate (highlight and shadow regions) is 91% for manta mines and 81% for cylinder mines.

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CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-339-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 339-341

Author(s):

R. Kaczynski ◽

A. Rylko

Keyword(s):

Supervised Classification ◽

Local Population ◽

Atmospheric Correction ◽

Ground Truth ◽

Landsat Tm ◽

Aerial Photographs ◽

Radiometric Calibration ◽

Topographic Map ◽

Ground Truth Data ◽

Color Composite

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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