An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

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Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images

Remote Sensing ◽

10.3390/rs12152349 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2349 ◽

Cited By ~ 3

Author(s):

Oleg Ieremeiev ◽

Vladimir Lukin ◽

Krzysztof Okarma ◽

Karen Egiazarian

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Visual Quality ◽

Quality Metrics ◽

Remote Sensing Images ◽

Opinion Score ◽

Different Types ◽

Reference Quality ◽

Improved Performance

Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented.

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Unsupervised labelling of remote sensing images based on force field clustering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210802 ◽

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.

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Improving the quality of remote sensing images using a universal reconstruction method

10.1117/12.816609 ◽

2008 ◽

Author(s):

Huanfeng Shen ◽

Tinghua Ai ◽

Pingxiang Li ◽

Yi Wang

Keyword(s):

Remote Sensing ◽

Reconstruction Method ◽

Remote Sensing Images

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070462 ◽

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.

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Distributed Framework for Processing High-Resolution Remote Sensing Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.j9976.109119 ◽

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.

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Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/10.35595/2414-9179-2021-2-27-191-204 ◽

2021 ◽

Vol 27 (2) ◽

pp. 191-204

Author(s):

Elina Sheremet ◽

Natalia Kalutskova ◽

Vladimir Dekhnich

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Visual Quality ◽

Aerial Photographs ◽

Main Task ◽

Field Observations ◽

North West ◽

Sensing Data ◽

Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

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Local Deep Descriptor for Remote Sensing Image Feature Matching

Remote Sensing ◽

10.3390/rs11040430 ◽

2019 ◽

Vol 11 (4) ◽

pp. 430 ◽

Cited By ~ 4

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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