scholarly journals Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization

2020 ◽  
Vol 12 (20) ◽  
pp. 3446 ◽  
Author(s):  
Chenxi Duan ◽  
Jun Pan ◽  
Rui Li
Keyword(s):  
Remote Sensing ◽  
Vertical Direction ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Series Of Experiments ◽  
Cloud Removal ◽  
Tensor Optimization ◽  
Cloud Mask ◽  
Temporal Direction

In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow as well as recover the cloud-contaminated pixels. Generally, the thick cloud and cloud shadow element are not only sparse but also smooth along the spatial horizontal and vertical direction, while the clean element is smooth along the temporal direction. Guided by the above insight, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity regularized tensor optimization (TSSTO) is proposed in this paper. Firstly, the sparsity norm is utilized to boost the sparsity of the cloud and cloud shadow element, and unidirectional total variation (UTV) regularizers are applied to ensure the smoothness in different directions. Then, through thresholding, the cloud mask and the cloud shadow mask can be acquired and used to guide the substitution. Finally, the reference image is selected to reconstruct details of the repairing area. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints.

scholarly journals Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1380
Author(s):  
Sen Wang ◽  
Xiaoming Sun ◽  
Pengfei Liu ◽  
Kaige Xu ◽  
Weifeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Geometric Distortion ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Speeded Up Robust Features ◽  
Adaptive Quantization ◽  
Texture Background

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.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

scholarly journals A Thin-Cloud Mask Method for Remote Sensing Images Based on Sparse Dark Pixel Region Detection

2018 ◽  
Vol 10 (4) ◽  
pp. 617 ◽  
Author(s):  
Wei Wu ◽  
Jiancheng Luo ◽  
Xiaodong Hu ◽  
Haiping Yang ◽  
Yingpin Yang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Region Detection ◽  
Thin Cloud ◽  
Dark Pixel ◽  
Cloud Mask

scholarly journals A Long Time-Series Radiometric Normalization Method for Landsat Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4505 ◽  
Author(s):  
Wei Wu ◽  
Xia Sun ◽  
Xianwei Wang ◽  
Jing Fan ◽  
Jiancheng Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Traditional Method ◽  
Acquisition Time ◽  
Reference Image ◽  
Optimization Strategy ◽  
Remote Sensing Images ◽  
Gray Scale ◽  
Long Time Series ◽  
Long Time

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)—radiometric characteristics of which do not change with time—are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.

scholarly journals Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

2020 ◽  
Vol 9 (7) ◽  
pp. 462
Author(s):  
Josephina Paul ◽  
B. Uma Shankar ◽  
Balaram Bhattacharyya
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Feature Fusion ◽  
Spectral Band ◽  
Minimum Energy ◽  
Low Frequency ◽  
Remote Sensing Images ◽  
Difference Image ◽  
Difference Images

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

scholarly journals Distributed Framework for Processing High-Resolution Remote Sensing Images

2019 ◽  
Vol 9 (1) ◽  
pp. 4287-4292
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Map Reduce ◽  
Distributed Model ◽  
Remote Sensing Images ◽  
Satellite Technology ◽  
Distributed Framework ◽  
Block Based ◽  
Hadoop Framework

Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.

scholarly journals Local Deep Descriptor for Remote Sensing Image Feature Matching

2019 ◽  
Vol 11 (4) ◽  
pp. 430 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Deep Learning ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Training Dataset ◽  
Feature Descriptor ◽  
Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

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