Geospatial Data for the Himalayan Region: Requirements, Availability, and Challenges

Agrawal, Shefali; Raghavendra, S.; Kumar, Shashi; Pande, Hina

doi:10.1007/978-981-13-2128-3_22

Cited by 4 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SAR is an active Earth observation system that can be installed on aircraft, satellites, spaceships and other flight platforms. It can observe the earth on all days and in all weather and it has a certain surface penetration ability [22][23][24]. SAR mainly records all kinds of backscatter responses produced by crop canopy biophysical structure.…”

Section: Introductionmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

show abstract

Section: Introductionmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Radiometric calibration is performed with internal and external calibrators to find the actual return of the radar signals from the objects present on the earth's surface 46 – 48 . The backscatter image represents the radar cross-section obtained after scattering from all the objects within the resolution cell 49 – 51 . The concerned space agencies generally provide the radiometric calibration formula to ensure the correct radar return from the different object classes in the SAR data because it is not possible for all the researchers and users to evaluate the radiometric properties with the help of active and passive calibrators.…”

Section: Resultsmentioning

confidence: 99%

Semantic segmentation of PolSAR image data using advanced deep learning model

Garg

Kumar

Bansal

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

show abstract