A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data

Mehra, Aryan; Jain, Nihal; Srivastava, H. S.

doi:10.1080/10106049.2019.1704072

Cited by 22 publications

(21 citation statements)

References 23 publications

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“…Due to the different nature of the SAR imagery systems, in many case studies, the conventional machine learning algorithms are unable to achieve comparable results as they obtain with the optical data. Despite the remarkable advances in the SAR data processing techniques, semantic information extraction from SAR data is still a challenge [42]. Moreover, Deep learning methods have provided immense opportunities for various computer vision applications and due to their remarkable performance, they have attracted wide attention in the remote sensing data processing community [43], [44].…”

Section: Scenario 3: Sentinel-1 Patch-based Annotation Analysismentioning

confidence: 99%

Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based Approach

Asiyabi

Datcu

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recent advances in remote sensing technology have provided (very) high spatial resolution Earth Observation (EO) data with abundant latent semantic information. Conventional data processing algorithms are not capable of extracting the latent semantic information form these data and harness their full potential. As a result, semantic information discovery methods, based on data mining techniques, such as Latent Dirichlet Allocation (LDA) and Bag of Visual Words (BOVW) models, can discover the latent information. Despite their crucial rule, there are only a few studies in the field of semantic data mining for remote sensing applications. This study is focused on this shortage. Three different scenarios are used to evaluate the semantic information discovery in various remote sensing applications, including both optical and Synthetic Aperture Radar (SAR) data with different spatial resolutions. In the first scenario, semantic discovery method correlated the semantic perception of the user and machine to correct and enhance the user defined Ground Truth (GT) map in very high-resolution RGB data. The potential of the semantic discovery is evaluated for wildfire affected area detection in Sentinel-2 data in the second scenario. Finally, in the third scenario, the semantic discovery method is utilized to detect the misclassifications as well as the patches with ambiguous or multiple semantic labels in a Sentinel-1 SAR patch-based benchmark dataset, to enhance the robustness and accuracy of the annotation in the dataset. Our results in these three scenarios demonstrated the capability of the data mining-based semantic information discovery methods for various remote sensing applications.

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Section: Scenario 3: Sentinel-1 Patch-based Annotation Analysismentioning

confidence: 99%

Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based Approach

Asiyabi

Datcu

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Data-level and decision-level fusion are the two most easily implemented information fusion methods, but their performance improvements are also limited. Recently, it has also an important research topic to comprehensively and effectively use a variety of information of radar data, such as multi-temporal [42] and multi-view [43] data, to achieve better model performance. An inverse synthetic aperture radar (ISAR) target recognition method based on both range profile (RP) data and ISAR images was proposed, based on decision-level fusion of the classification results of RP data and ISAR images [44].…”

Section: Information Fusionmentioning

confidence: 99%

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Guo

et al. 2021

Remote Sensing

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Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

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“…Satellite altimeter can provide statistical information of surface eddies by detecting sea surface height anomalies (SSHA) [ 20 ], sea level anomalies (SLA) [ 21 ], or sea surface temperature (SST) [ 22 ], which is then applied to compute the lifetime, eddy radius, spatial distribution, trajectory, and vorticity of each eddy. The studies on oceanic eddies develop rapidly due to the increasing open datasets for oceanographic researches, e.g., the Copernicus marine environment monitoring service (CMEMS) datasets [ 7 ]. However, only a small number of satellites have sufficient resolution to provide accurate eddy measurements, and satellite-altimeter-based eddy studies tend to focus on specific regions where the observation data are sufficient for investigation, for example, the Mediterranean and Australian Coral Sea region.…”

Section: Related Workmentioning

confidence: 99%

“…To extract the characteristic information, different equipment can be used to collect data. The popular observation equipment includes in-situ sensors, satellite altimeters [ 7 ], and high-frequency radars (HFRs) [ 8 ], each providing valuable observations with different spatial resolutions. The in-situ sensors have an extremely high resolution but are difficult and inefficient to track the spatial variation in a large area.…”

Section: Introductionmentioning

confidence: 99%

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Liu

Zhou

Wen

2020

Sensors

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Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

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A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data

Cited by 22 publications

References 23 publications

Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based Approach

Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based Approach

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

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