An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images

2020 ◽  
Vol 11 (2) ◽  
pp. 201-214 ◽  
Author(s):  
Shoulin Yin ◽  
Hang Li ◽  
Lin Teng ◽  
Man Jiang ◽  
Shahid Karim
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Optical Remote Sensing ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Airport Detection

scholarly journals A Deep Multiscale Fusion Method via Low-Rank Sparse Decomposition for Object Saliency Detection Based on Urban Data in Optical Remote Sensing Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Cheng Zhang ◽  
Dan He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Saliency Detection ◽  
Saliency Map ◽  
Low Rank ◽  
Optical Remote Sensing ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Sparse Decomposition ◽  
Depth Feature

The urban data provides a wealth of information that can support the life and work for people. In this work, we research the object saliency detection in optical remote sensing images, which is conducive to the interpretation of urban scenes. Saliency detection selects the regions with important information in the remote sensing images, which severely imitates the human visual system. It plays a powerful role in other image processing. It has successfully made great achievements in change detection, object tracking, temperature reversal, and other tasks. The traditional method has some disadvantages such as poor robustness and high computational complexity. Therefore, this paper proposes a deep multiscale fusion method via low-rank sparse decomposition for object saliency detection in optical remote sensing images. First, we execute multiscale segmentation for remote sensing images. Then, we calculate the saliency value, and the proposal region is generated. The superpixel blocks of the remaining proposal regions of the segmentation map are input into the convolutional neural network. By extracting the depth feature, the saliency value is calculated and the proposal regions are updated. The feature transformation matrix is obtained based on the gradient descent method, and the high-level semantic prior knowledge is obtained by using the convolutional neural network. The process is iterated continuously to obtain the saliency map at each scale. The low-rank sparse decomposition of the transformed matrix is carried out by robust principal component analysis. Finally, the weight cellular automata method is utilized to fuse the multiscale saliency graphs and the saliency map calculated according to the sparse noise obtained by decomposition. Meanwhile, the object priors knowledge can filter most of the background information, reduce unnecessary depth feature extraction, and meaningfully improve the saliency detection rate. The experiment results show that the proposed method can effectively improve the detection effect compared to other deep learning methods.

FRF-Net: Land Cover Classification From Large-Scale VHR Optical Remote Sensing Images

2020 ◽  
Vol 17 (6) ◽  
pp. 1057-1061 ◽  
Author(s):  
Qianbo Sang ◽  
Yin Zhuang ◽  
Shan Dong ◽  
Guanqun Wang ◽  
He Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Large Scale ◽  
Land Cover Classification ◽  
Optical Remote Sensing ◽  
Remote Sensing Images

scholarly journals Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3622
Author(s):  
Xu He ◽  
Shiping Ma ◽  
Linyuan He ◽  
Le Ru ◽  
Chen Wang
Keyword(s):  
Remote Sensing ◽  
Aspect Ratio ◽  
Object Detection ◽  
Large Scale ◽  
Large Aspect Ratio ◽  
Remote Sensing Images ◽  
Orientation Error ◽  
Multi Scale ◽  
Half Axis ◽  
Oriented Object

Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.

Airport Detection Based on a Multiscale Fusion Feature for Optical Remote Sensing Images

2017 ◽  
Vol 14 (9) ◽  
pp. 1469-1473 ◽  
Author(s):  
Zhifeng Xiao ◽  
Yiping Gong ◽  
Yang Long ◽  
Deren Li ◽  
Xiaoying Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Airport Detection ◽  
Fusion Feature

scholarly journals A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

2020 ◽  
Vol 12 (19) ◽  
pp. 3115 ◽  
Author(s):  
Liqiong Chen ◽  
Wenxuan Shi ◽  
Cien Fan ◽  
Lian Zou ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
False Alarms ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Dense Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Coarse To Fine

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

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