Automatic recognition of airfield runways based on Radon transform and hypothesis testing in SAR images

Xiong, Wei; Zhong, Juanjuan; Zhou, Ye

doi:10.1109/gsmm.2012.6314098

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…Previous researches focused on airport extraction using a priori knowledge and limited feature design. Xiong et al proposed an airport runway recognition method in SAR images based on Radon transform and hypothesis testing [8]. Di et al extracted the airport using a combination of improved chain codes based edge tracking and the Hough transform [9].…”

Section: A Traditional Airport Extraction Methodsmentioning

confidence: 99%

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CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes

Wu,

Cai,

Wang

2024

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

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Airports represent essential infrastructure, offering substantial research and application potential. However, extracting airports from complex Synthetic Aperture Radar (SAR) scenes is challenging due to the cluttered background and fine structure of airports. This necessitates the integration of global and local information for fine-grained extraction. To tackle this issue, this paper introduces a novel framework for fine-grained extraction of airports from large-scale SAR images. First, a CNN-Transformer hybrid semantic segmentation network with multi-scale contextual fusion is proposed, named CTMANet. In this network, the encoder combines CNNs and Transformers to capture local and global information, while the Multi-Scale Context Aggregation (MCA) block fuses multi-scale contextual information. Skip connections between the encoder and decoder are established to minimize the loss of detailed information and fuse low-level features with high-level semantic features. Moreover, a Category Balance block is designed to address class imbalance. Experimental results on the GF-3 dataset demonstrate that CTMANet outperforms state-of-the-art methods, proving its superior suitability for fine-grained airport extraction in large-scale scenarios.

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Section: A Traditional Airport Extraction Methodsmentioning

confidence: 99%

“…There are three primary categories of traditional approaches, but each has some clear flaws. 1) Extract the airport through the runway's edges [8]- [10]. Such methods merely use the most obvious low-level features of airport and are only effective for small scene images.…”

Section: Introductionmentioning

confidence: 99%

CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes

Wu,

Cai,

Wang

2024

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

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“…For the first type of features, most researchers use the method of linear feature detection. Kou et al [5] proposed an airport detection method from remotely sensed images based on line segment detectors, and Xiong et al [6] presented a detection algorithm of airports from SAR images based on random transform and hypothesis testing. These methods rely on line segment detector (LSD) transform, random transform, or other transformation methods to obtain the linear edge segments of airports, which are then stitched for airport identification.…”

Section: State-of-the-artmentioning

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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“…Three levels of Gaussian pyramid of airports images are constructed, edge detection is carried out by canny edge operator and for each scale of image straight lines are extracted using Radon Transformation [1], [2]. Objects with significant major and minor axis with maximum area are taken as potential candidates.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…Previous research is focused to detect and recognize airstrips at single resolution level based on airstrip characteristics such as longest parallel straight lines [1], [2], [3], high local intensities [1], dimensions [4], [5], [6], large grey area [2], [3] and texture segmentation [5], [6]. In small sized images, segmentation from background is carried out using line detection, pattern matching techniques [4], whereas in cluttered backgrounds Kernel matching persuits (KMP) [5] and Adaboost [6] classifiers are trained for complex feature vectors of all textures and airstrip texture is then segmented from the background.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of isolated airstrips in multiscale satellite images using radon transformation and support vector machine

Kausar

Javaid

Rao

et al. 2015

2015 National Software Engineering Conference (NSEC)

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The work presents an algorithm for recognition of isolated airstrips in multiscale Google satellite images. First, strong straight lines are detected by Radon Transform and airstrip detection is accomplished by detecting longest straight lines in multiresolution images at different altitudes. Later normalized crosscorrelation is used to find the degree of similarity among multiscale airstrip patterns. Finally, support vector machines are used to recognize airstrips. The proposed technique shows promising results in classifying airstrips from other commonly appearing objects in optical images taken from satellites i.e. roads, canals, large buildings, etc. Algorithm is dually tested on multiresolution images captured using various cameras at different heights and have produced similar results.

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Automatic recognition of airfield runways based on Radon transform and hypothesis testing in SAR images

Cited by 11 publications

References 4 publications

CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes

CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes

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

Automatic recognition of isolated airstrips in multiscale satellite images using radon transformation and support vector machine

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