scholarly journals Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

2020 ◽  
Vol 12 (15) ◽  
pp. 2455
Author(s):  
Kazi Aminul Islam ◽  
Mohammad Shahab Uddin ◽  
Chiman Kwan ◽  
Jiang Li
Keyword(s):  
Domain Adaptation ◽  
Human Life ◽  
Ground Truth ◽  
Accurate Estimation ◽  
Support Vector ◽  
Learning Approaches ◽  
Radar Images ◽  
Response Team ◽  
Multi Temporal ◽  
Flood Detection

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

scholarly journals Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

2017 ◽  
Vol IV-1/W1 ◽  
pp. 35-42 ◽  
Author(s):  
C. H. Yang ◽  
Y. Pang ◽  
U. Soergel
Keyword(s):  
Change Detection ◽  
Ground Truth ◽  
Night Vision ◽  
City Management ◽  
Persistent Scatterer Interferometry ◽  
Spatiotemporal Resolution ◽  
Sar Images ◽  
Construction Monitoring ◽  
Radar Images ◽  
Multi Temporal

Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.

scholarly journals Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2745 ◽  
Author(s):  
Alberto Refice ◽  
Marina Zingaro ◽  
Annarita D’Addabbo ◽  
Marco Chini
Keyword(s):  
Land Cover ◽  
Visual Analysis ◽  
Ground Cover ◽  
Ground Truth ◽  
Integrated Analysis ◽  
Corine Land Cover ◽  
Multi Temporal ◽  
L Band ◽  
Flood Detection ◽  
Sar Imagery

Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively. Information from the globally available CORINE land cover dataset, derived over Africa from the Proba V satellite, and available publicly at the resolution of 100 m, is also exploited. Integrated multi-frequency, multi-temporal, and multi-polarizations analysis allows highlighting different drying dynamics for floodwater over various land cover classes, such as herbaceous vegetation, wetlands, and forests. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site. The approach is validated through visual analysis from Google EarthTM imagery. This kind of integrated analysis, exploiting multi-source remote sensing to partially make up for the unavailability of reliable ground truth, is expected to assume increasing importance as constellations of satellites, observing the Earth in different electromagnetic radiation bands, will be available.

crops planting area identification and analysis based on multi-source high resolution remote sensing data

2020 ◽  
Author(s):  
Lei Wang ◽  
Haoran Sun ◽  
Wenjun Li ◽  
Liang Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Random Forest Method ◽  
Multi Temporal ◽  
Sar Data

<p>Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images,  efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.</p>

scholarly journals Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

2021 ◽  
Vol 25 (2) ◽  
pp. 603-618
Author(s):  
Mohammad Taghi Sattari ◽  
Halit Apaydin ◽  
Shahab S. Band ◽  
Amir Mosavi ◽  
Ramendra Prasad
Keyword(s):  
Minimum Temperature ◽  
Short Term Memory ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Accurate Estimation ◽  
Support Vector ◽  
Learning Approaches ◽  
Ann Model ◽  
Average Maximum ◽  
Semi Arid

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.

scholarly journals EXPLOITING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR FLOOD EXTEND MAPPING

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 109-113 ◽  
Author(s):  
C. Bayik ◽  
S. Abdikan ◽  
G. Ozbulak ◽  
T. Alasag ◽  
S. Aydemir ◽  
...  
Keyword(s):  
Rural Areas ◽  
Emergency Services ◽  
Global Climate ◽  
Human Life ◽  
Spatial Dynamics ◽  
Dynamic Structure ◽  
Learning Approaches ◽  
Multi Temporal ◽  
Sar Data ◽  
Flooded Areas

<p><strong>Abstract.</strong> Recently, global climate change is one of the biggest challenges in the world. Dense downfall and following catastrophic floods are one of the most destructive natural hazards among all. Consequences do not only risk human life but also cause economical damage. It is critical rapid mapping of flooding for decision making and emergency services in river management. In this study, we apply a multi-temporal change detection analysis to investigate the flooded areas occurred in Edirne province of Turkey. The study area is located at the lower course of Meric River (Evros in Greece or Maritsa in Bulgarian) which is the border between Turkey and Greece. The river basin is dominated by cropland which suffers from strong catastrophic precipitation. This situation cause overflow of capacity of the dams located along the river and serious flooding occur. Due to its dynamic structure the region exposed to heavy flooding in the past. One of the biggest inundations was occurred at 2nd February 2015 which resulted severe devastation in both urban and rural areas. For the analyses of the temporal and spatial dynamics of the disaster we use Sentinel-1 Synthetic Aperture Radar (SAR) data due to its systematic frequent acquisition. A dataset of pre-event and post-event Sentinel-1 images within the January and February of 2015 period was acquired. Flooded areas were extracted with threshold, random forest and deep learning approaches.</p>

scholarly journals Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection

2020 ◽  
Vol 12 (11) ◽  
pp. 1720
Author(s):  
Thibault Taillade ◽  
Laetitia Thirion-Lefevre ◽  
Régis Guinvarc’h
Keyword(s):  
Change Detection ◽  
Signal To Noise Ratio ◽  
Open Data ◽  
Ground Truth ◽  
Critical Issue ◽  
Detection Methods ◽  
Reference Image ◽  
Radar Images ◽  
Multi Temporal ◽  
Disaster Monitoring

Change detection (CD) in SAR (Synthethic Aperture Radar) images has been widely studied in recent years and has become increasingly attractive due to the growth of available datasets. The potential of CD has been shown in different fields, including disaster monitoring and military applications. Access to multi-temporal SAR images of the same scene is now possible, and therefore we can improve the performance and the interpretation of CD. Apart from specific SAR campaign measurements, the ground truth of the scene is usually unknown or only partially known when dealing with open data. This is a critical issue when the purpose is to detect targets, such as vehicles or ships. Indeed, typical change detection methods can only provide relative changes; the actual number of targets on each day cannot be determined. Ideally, this change detection should occur between a target-free image and one with the objects of interest. To do so, we propose to benefit from pixels’ intrinsic temporal behavior to compute a frozen background reference (FBR) image and perform change detection from this reference image. We will then consider that the scene consists only of immobile objects (e.g., buildings and trees) and removable objects that can appear and disappear from acquisition to another (e.g., cars and ships). Our FBR images will, therefore, aim to estimate the immobile background of the scene to obtain, after change detection, the exact amount of targets present on each day. This study was conducted first with simulated SAR data for different number of acquisition dates and Signal-to-Noise Ratio (SNR). We presented an application in the region of Singapore to estimate the number of ships in the study area for each acquisition.

scholarly journals NEAR-REALTIME FLOOD DETECTION FROM MULTI-TEMPORAL SENTINEL RADAR IMAGES USING ARTIFICIAL INTELLIGENCE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1663-1670
Author(s):  
R. M. de la Cruz ◽  
N. T. Olfindo Jr. ◽  
M. M. Felicen ◽  
N. J. B. Borlongan ◽  
J. K. L. Difuntorum ◽  
...  
Keyword(s):  
Large Scale ◽  
The Philippines ◽  
Human Intervention ◽  
Emergency Situations ◽  
Radar Images ◽  
Optical Images ◽  
Multi Temporal ◽  
Computationally Intensive ◽  
Flood Detection ◽  
Flooded Areas

Abstract. Flood extent delineation from RADAR images usually entails manual thresholding per scene, which is not feasible when tackling large-scale floods that often covers multiple RADAR scenes. It is also computationally intensive when processed through traditional remote sensing techniques that limit its use during emergency situations. To hasten the production of flood maps from RADAR images during flooding incidents, a deep learning model using Fully connected Convolutional Neural Network (FCNN) has been developed to delineate flooded areas with minimal human intervention. The model was formulated from the data gathered during a flooding event captured by both Sentinel-1A SAR satellite and Planet’s Dove optical satellites. Two pre-flood and one post-flood SAR scenes were used to detect the occurrence of water by analysing drops in backscatter values. The potential flood extents were verified using optical images which were then used to train the AI model. The model is currently being used operationally to map flood extent across the Philippines with no human intervention from data download to detection of flooded areas. The technique can detect floods across five Sentinel 1 scenes in less than four hours upon download of new satellite data.

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