Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

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Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽

10.3390/w12020417 ◽

2020 ◽

Vol 12 (2) ◽

pp. 417 ◽

Cited By ~ 2

Author(s):

Mohamed Abdelkareem ◽

Fathy Abdalla ◽

Samar Y. Mohamed ◽

Farouk El-Baz

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Volcanic Activity ◽

Remote Sensing Data ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Alos Palsar ◽

Sensing Data ◽

The Past ◽

Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

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Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2017.1346468 ◽

2017 ◽

Vol 43 (4) ◽

pp. 360-373 ◽

Cited By ~ 23

Author(s):

Meisam Amani ◽

Bahram Salehi ◽

Sahel Mahdavi ◽

Jean Elizabeth Granger ◽

Brian Brisco ◽

...

Keyword(s):

Remote Sensing ◽

Newfoundland And Labrador ◽

Remote Sensing Data ◽

Wetland Classification ◽

Optical Remote Sensing ◽

Sensing Data ◽

Multi Temporal

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Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sensors ◽

10.3390/s19102401 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2401 ◽

Cited By ~ 16

Author(s):

Chuanliang Sun ◽

Yan Bian ◽

Tao Zhou ◽

Jianjun Pan

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Crop Growth ◽

Landsat 8 ◽

Growth Status ◽

Sensing Data ◽

Crop Type ◽

Multi Temporal ◽

Crop Types ◽

Sentinel 2

Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

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Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

Remote Sensing ◽

10.3390/rs12244037 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4037

Author(s):

Zhi Li ◽

Xiaomei Yang

Keyword(s):

Remote Sensing ◽

Surface Water ◽

High Resolution ◽

Remote Sensing Data ◽

Automated Detection ◽

Urban Surface ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Sensing Data ◽

Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

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Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

Remote Sensing ◽

10.3390/rs12050832 ◽

2020 ◽

Vol 12 (5) ◽

pp. 832 ◽

Cited By ~ 1

Author(s):

Chunhua Liao ◽

Jinfei Wang ◽

Qinghua Xie ◽

Ayman Al Baz ◽

Xiaodong Huang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Convolutional Neural Network ◽

Remote Sensing Data ◽

Support Vector ◽

Learning Methods ◽

Sensing Data ◽

Multi Temporal ◽

Crop Classification

Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D.

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Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data

Land ◽

10.3390/land10020100 ◽

2021 ◽

Vol 10 (2) ◽

pp. 100

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Siti Saringatin ◽

Pramaditya Wicaksono ◽

Bachtiar Wahyu Mutaqin ◽

...

Keyword(s):

Remote Sensing ◽

Coastal Erosion ◽

Remote Sensing Data ◽

Google Earth ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Coastal Regions ◽

Shoreline Changes ◽

Sensing Data ◽

Rates Of Change

Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.

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A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data

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

10.5194/isprsarchives-xl-7-15-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 15-18 ◽

Cited By ~ 7

Author(s):

U. H. Atasever ◽

P. Civicioglu ◽

E. Besdok ◽

C. Ozkan

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Remote Sensing Data ◽

Optical Remote Sensing ◽

Sensing Data ◽

Backtracking Search ◽

Search Optimization ◽

Detection Approach ◽

Multi Temporal ◽

Backtracking Search Optimization Algorithm

Change detection is one of the most important subjects of remote sensing discipline. In this paper, a new unsupervised change detection approach is proposed for multi-temporal remotely sensed optic imagery. This approach does not require any prior information about changed and unchanged pixels. The approach is based on Discrete Wavelet Transform (DWT) based image fusion and Backtracking Search Optimization Algorithm (BSA). In the first step of the approach, absolute-valued difference image and absolute-valued log-ratio image is calculated from co-registered and radiometrically corrected multi-temporal images. Then, these difference images are fused using DWT. The fused image is filtered by median filter for edge information preservation and by wiener filter for image smoothing. Then, a min-max normalization is applied to the filtered data. The normalized data is clustered into two groups with BSA as changed and unchanged pixels by minimizing an objective function, unlike classical methods using CVA, PCA, FCM or K-means techniques. To show effectiveness of proposed approach, two remote sensing data sets, Sardinia and Mexico, are used. False Alarm, Missed Alarm, Total Alarm and Total Error Rate are selected as performance criteria to evaluate the effectiveness of new approach using ground truth images. Experimental results show that proposed approach is effective for unsupervised change detection of optical remote sensing data.

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Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133316639403 ◽

2016 ◽

Vol 40 (2) ◽

pp. 322-351 ◽

Cited By ~ 20

Author(s):

Jadunandan Dash ◽

Booker O. Ogutu

Keyword(s):

Remote Sensing ◽

Terrestrial Ecosystem ◽

Remote Sensing Data ◽

Terrestrial Ecosystems ◽

Global Scale ◽

Data Availability ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Ecosystem Monitoring ◽

Sensing Data

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.

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On the potential of multi-source remote sensing data in characterizing the Total Suspended Matter variability in inland waters.

10.5194/egusphere-egu21-14711 ◽

2021 ◽

Author(s):

Emanuele Ciancia ◽

Alessandra Campanelli ◽

Teodosio Lacava ◽

Angelo Palombo ◽

Simone Pascucci ◽

...

Keyword(s):

Remote Sensing ◽

Methodological Approach ◽

Remote Sensing Data ◽

Spatiotemporal Variability ◽

Water Level Fluctuations ◽

Inland Waters ◽

Landsat 8 ◽

Ground Truth Data ◽

Sensing Data ◽

Multi Temporal

The assessment of TSM spatiotemporal variability plays a key role in inland water management, considering how these fluctuations affect water transparency, light availability, and the physical, chemical, and biological processes. All the above-mentioned topics highlight the need to develop innovative methodologies of data analysis that are able to handle multi-mission and multi-source remote sensing data, fostering the implementation of integrated and sustainable approaches. Sentinel-2A multispectral instrument (MSI) and Landsat 8 operational land instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) mainly owing to their spatial resolution (10&#8211;60 m). Furthermore, the joint use of these sensors offers the opportunity to build time series with an improved revisiting time thus enabling limnologists, aquatic ecologists and water resource managers to enhance their monitoring efforts. In this framework, the potential of MSI&#8211;OLI combined data in characterizing the multi-temporal (2014&#8211;2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy) has been evaluated in this work. In particular, a customized MSI-based TSM model (R2=0.81) has been developed and validated by using ground truth data acquired during specific measurement campaigns. The model was then exported on OLI data through an inter-calibration procedure (R2=0.87), allowing for the generation of a TSM multi-temporal MSI&#8211;OLI merged dataset. The analysis of the derived multi-year TSM monthly maps has shown the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side appears more affected by inflowing rivers and water level fluctuations, whose &#160;effects&#160; tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results highlight how the proposed methodological approach (i.e. in situ data collection, satellite data processing and modeling) can be exported in other inland waters that deserve to be investigated for a better management of water quality and monitoring systems.

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