scholarly journals Accurate Localization of Oil Tanks in Remote Sensing Images via FGMRST-Based CNN

2021 ◽  
Vol 13 (22) ◽  
pp. 4646
Author(s):  
Han Jiang ◽  
Yueting Zhang ◽  
Jiayi Guo ◽  
Fangfang Li ◽  
Yuxin Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radial Symmetry ◽  
Object Localization ◽  
Parameter Method ◽  
Remote Sensing Images ◽  
Structure Information ◽  
Accurate Localization ◽  
Oil Storage ◽  
Order Of Magnitude ◽  
Oil Tank

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.

Study on Remote Sensing Faults Information Extraction in Eastern Kunlun, NW China

2013 ◽  
Vol 380-384 ◽  
pp. 3958-3961
Author(s):  
Xiao Hu Zhou
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Structural Information ◽  
Shear Zones ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Orogenic Belt ◽  
Remote Sensing Images ◽  
Structure Information ◽  
Geological Body

Choosing the junction of Altun-Kunlun orogenic belt as the anatomical area of extracting complex texture and structure information from remote sensing images, make full use of multi-band remote sensing images to reflect the characteristics of the properties, to extract hidden information through image processing. Analyzing the structure elements by geological body, rock combination, linear and banded structure, penetrative and non-penetrative planar structure, folds, to carry out the surficial composition and structure research of the the junction of Altun-Kunlun orogenic belt, identifying different geological bodies, the fault zones, ductile shear zones, superimposed folds and different strain zones, the different types of foliation, clarifying the characteristics of multi-source remote sensing image from the angle of the image processing methods, proposing new remote sensing image extraction methods and recognition of structural information technology and new understanding of the regional geology.

scholarly journals Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2702 ◽  
Author(s):  
Shuxin Li ◽  
Zhilong Zhang ◽  
Biao Li ◽  
Chuwei Li
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Parameter Method ◽  
Remote Sensing Images ◽  
Precise Location ◽  
Bounding Box ◽  
Testing Stage ◽  
Close Proximity ◽  
Orientation Information ◽  
Bounding Boxes

Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.

scholarly journals Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1689
Author(s):  
Danjun Deng
Keyword(s):  
Remote Sensing ◽  
Design Method ◽  
Initial Point ◽  
Remote Sensing Image ◽  
Mean Value ◽  
Gradient Structure ◽  
Feature Points ◽  
Remote Sensing Images ◽  
Shape Distortion ◽  
Structure Information

Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.

scholarly journals Building Extraction from Remote Sensing Images with Sparse Token Transformers

2021 ◽  
Vol 13 (21) ◽  
pp. 4441
Author(s):  
Keyan Chen ◽  
Zhengxia Zou ◽  
Zhenwei Shi
Keyword(s):  
Remote Sensing ◽  
Computational Complexity ◽  
State Of The Art ◽  
Feature Space ◽  
Extraction Methods ◽  
Aerial Image ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Order Of Magnitude ◽  
Image Pixels

Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.

Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

2013 ◽  
Vol 63 (3) ◽  
pp. 298-304 ◽  
Author(s):  
Naveen Kushwaha ◽  
Debasis Chaudhuri ◽  
Manish Singh
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Oil Tank

scholarly journals Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection

2019 ◽  
Vol 9 (17) ◽  
pp. 3583
Author(s):  
Fen Cai ◽  
Miao-Xia Guo ◽  
Li-Fang Hong ◽  
Ying-Yi Huang
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Research Area ◽  
Reconstruction Error ◽  
Remote Sensing Images ◽  
Discriminative Ability ◽  
Structure Information ◽  
Label Information ◽  
Sparsity Preserving Projection ◽  
Hyperspectral Remote Sensing Images

Dimensionality reduction is an important research area for hyperspectral remote sensing images due to the redundancy of spectral information. Sparsity preserving projection (SPP) is a dimensionality reduction (DR) algorithm based on the l1-graph, which establishes the relations of samples by sparse representation. However, SPP is an unsupervised algorithm that ignores the label information of samples and the objective function of SPP; instead, it only considers the reconstruction error, which means that the classification effect is constrained. In order to solve this problem, this paper proposes a dimensionality reduction algorithm called the supervised sparse embedded preserving projection (SSEPP) algorithm. SSEPP considers the manifold structure information of samples and makes full use of the label information available in order to enhance the discriminative ability of the projection subspace. While maintaining the sparse reconstruction error, the algorithm also minimizes the error between samples of the same class. Experiments were performed on an Indian Pines hyperspectral dataset and HJ1A-HSI remote sensing images from the Zhangjiang estuary in Southeastern China, respectively. The results show that the proposed method effectively improves its classification accuracy.

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