A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration

Chen, Lifu; Cui, Xianliang; Li, Zhenhong; Yuan, Zhihui; Jin, Xing; Xing, Xuemin; Jia, Zhiwei

doi:10.3390/s19112479

Cited by 21 publications

(22 citation statements)

References 52 publications

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“…The attention module can fuse the input global information, and is widely used in the field of image vision. Chen et al [32] proposed a feature recalibration network with multi-level spatial features (FRN-MSF) to implement scene classification for 11 types of scenes from SAR images, which incurred the SENet and achieved a satisfactory classification result. Fu et al [33] extended two types of attention modules based on the self-attention module, and constructed the position attention module (PAM) and channel attention module (CAM), which work in parallel to capture the global information of the image in the spatial and channel dimensions to obtain rich contextual information.…”

Section: Dual-attention Mechanismmentioning

confidence: 99%

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

et al. 2020

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The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.

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Section: Dual-attention Mechanismmentioning

confidence: 99%

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

et al. 2020

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“…The Airport detection has a wide range of applications in navigation, accident detection, rescue, and aircraft positioning [4]. Existing research of airport detection is mostly based on optical remote sensing images [5] [6]. The traditional method of extracting the airport edge line segment to perform the airport detection is the most commonly used [5]- [8], but this method assumes that linear features could be easily extracted from all airports.…”

Section: Iistate Of the Artmentioning

confidence: 99%

“…Existing research of airport detection is mostly based on optical remote sensing images [5] [6]. The traditional method of extracting the airport edge line segment to perform the airport detection is the most commonly used [5]- [8], but this method assumes that linear features could be easily extracted from all airports. This is very challenging for those airports with numerous terminals and irregular buildings.…”

Section: Iistate Of the Artmentioning

confidence: 99%

Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery

et al. 2020

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The automatic extraction of airport runway areas from high-resolution Synthetic Aperture Radar (SAR) images is of great research significance in the military and civilian fields. However, it is still challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework is proposed to extract airport runway areas (runways, taxiways, packing lots, and aircrafts) in a fast and automatic manner. The framework is based on the Geospatial Contextual Attention Mechanism (GCAM) for geospatial feature learning and classification, which is employed together with the downsampling and coordinate mapping modules. To evaluate the performance of the proposed framework, three large-scale Gaofen-3 SAR images with 1m resolution are utilized in the experiment. According to the results, Mean Pixels Accuracy (MPA) and Mean Intersection Over Union (MIOU) of the GCAM are 0.9850 and 0.9536, respectively, which outperform RefineNet, DeepLabV3+, and MDDA. The training time of GCAM for the dataset is 2.25h, and the average testing time for the five SAR images is only 18.15s. Therefore, GCAM can offer rapid and automatic airport detection from high-resolution SAR images with high accuracy, which can further be employed to mark the airport to greatly improve the detection accuracy of the aircrafts.

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“…Scenery images comprise a wide variety of knowledge about the behavior of various objects which have visible features such as borders, corners, and point clouds and these enable us to learn, modify, consider alternative solutions and create new techniques to examine complex scenes. Scene interpretation [ 1 , 2 ] should be capable of accommodating changes in the environment being observed, identifying the vital characteristics of various objects and defining relationships among various objects in order to represent the actual scene behaviors [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Ahmed

Jalal

Kim

2020

Sensors

100

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In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

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A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration

Cited by 21 publications

References 52 publications

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

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