scholarly journals Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

2021 ◽  
Vol 13 (23) ◽  
pp. 13475
Author(s):  
Boce Chu ◽  
Feng Gao ◽  
Yingte Chai ◽  
Yu Liu ◽  
Chen Yao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Image Filtering ◽  
Remote Sensing Image ◽  
User Preferences ◽  
Large Area ◽  
Filtering Algorithm ◽  
Full Coverage ◽  
Filtering Effect ◽  
Image Collection

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

scholarly journals Image Collection Simulation Using High-Resolution Atmospheric Modeling

2020 ◽  
Vol 12 (19) ◽  
pp. 3214
Author(s):  
Andrew Kalukin ◽  
Satoshi Endo ◽  
Russell Crook ◽  
Manoj Mahajan ◽  
Robert Fennimore ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Model ◽  
Remote Sensing Image ◽  
Atmospheric Modeling ◽  
Atmospheric Effects ◽  
Multispectral Imagery ◽  
Modeling Framework ◽  
Voxel Grid ◽  
Image Collection

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

A User Preferences Modeling for Intellegent Dissemination of Remote Sensing Image

2014 ◽  
Vol 519-520 ◽  
pp. 548-552
Author(s):  
Chun Hui Zhou ◽  
Gou Jun Luo ◽  
Di Chen ◽  
Yu Xia ◽  
Li Wen Huang
Keyword(s):  
Remote Sensing ◽  
Time Factors ◽  
User Profile ◽  
Remote Sensing Image ◽  
Computing Methods ◽  
User Preferences ◽  
Time Space ◽  
Novel Approach ◽  
User Demand ◽  
Demand Preference

In order to achieve the intelligent dissemination of remote sensing image, the primary task is to establish a suitable user profile. In this paper, we proposed a novel approach of modeling user demand preferences, and took into account the multiple interests and the time factors. And we presented some computing methods of feature preference including time, space, image parameters, etc. At last, the simulation example shows the feasibility and effectiveness of the designed user demand preference model.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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