Aircraft Detection from Remote Sensing Images using Single Shot Scale-invariant Face Detector with Viridis Saliency Map

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

Download Full-text

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Remote Sensing ◽

10.3390/rs12010152 ◽

2020 ◽

Vol 12 (1) ◽

pp. 152 ◽

Cited By ~ 6

Author(s):

Ting Nie ◽

Xiyu Han ◽

Bin He ◽

Xiansheng Li ◽

Hongxing Liu ◽

...

Keyword(s):

Remote Sensing ◽

Fourier Transform ◽

Detection Method ◽

Saliency Detection ◽

Visual Saliency ◽

Saliency Map ◽

False Alarms ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

Download Full-text

An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13173425 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3425

Author(s):

Xin Zhao ◽

Hui Li ◽

Ping Wang ◽

Linhai Jing

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Image Registration ◽

Feature Descriptor ◽

Remote Sensing Images ◽

Residual Network ◽

Scale Invariant ◽

Registration Method ◽

Sensing Applications ◽

Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

Download Full-text

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2011.2144607 ◽

2011 ◽

Vol 49 (11) ◽

pp. 4516-4527 ◽

Cited By ~ 159

Author(s):

Amin Sedaghat ◽

Mehdi Mokhtarzade ◽

Hamid Ebadi

Keyword(s):

Remote Sensing ◽

Feature Matching ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Scale Invariant ◽

Invariant Feature ◽

Scale Invariant Feature

Download Full-text

Valid Aircraft Detection System for Remote Sensing Images Based on Cognitive Models

Acta Optica Sinica ◽

10.3788/aos201838.0111005 ◽

2018 ◽

Vol 38 (1) ◽

pp. 0111005

Author(s):

侯宇青阳 Hou Yuqingyang ◽

全吉成 Quan Jicheng ◽

魏湧明 Wei Yongming

Keyword(s):

Remote Sensing ◽

Detection System ◽

Cognitive Models ◽

Remote Sensing Images ◽

Aircraft Detection

Download Full-text

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

Wireless Communications and Mobile Computing ◽

10.1155/2020/7917021 ◽

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.

Download Full-text