Intelligent System for Feature Extraction of Oil Slick in SAR Images: Speckle Filter Analysis

2006 ◽  
pp. 729-736
Author(s):  
Danilo L. de Souza ◽  
Adrião D. D. Neto ◽  
Wilson da Mata
Keyword(s):  
Feature Extraction ◽  
Intelligent System ◽  
Sar Images ◽  
Oil Slick ◽  
Filter Analysis

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

An accurate oil slick detection method for SAR images

2018 ◽  
Author(s):  
Qian Zhang ◽  
Yunlin Huang ◽  
Jifang Pei ◽  
Junjie Wu ◽  
Haiguang Yang ◽  
...  
Keyword(s):  
Detection Method ◽  
Sar Images ◽  
Oil Slick

A level set method for oil slick segmentation in SAR images

2005 ◽  
Vol 26 (6) ◽  
pp. 1145-1156 ◽  
Author(s):  
B. Huang ◽  
H. Li ◽  
X. Huang
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Sar Images ◽  
Oil Slick

Combination of ELM and BEMD for Target Recognition of SAR Images

2021 ◽  
Vol 30 (13) ◽  
Author(s):  
Zhichao Liu ◽  
Baida Qu
Keyword(s):  
Feature Extraction ◽  
Embedded System ◽  
High Efficiency ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Sar Images ◽  
Stationary Target

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images

2018 ◽  
Vol 14 (12) ◽  
pp. 5530-5538 ◽  
Author(s):  
Ning Lv ◽  
Chen Chen ◽  
Tie Qiu ◽  
Arun Kumar Sangaiah
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Change Detection ◽  
Sar Images
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