scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

scholarly journals Cumulative Mean Intensity Differential Transition Algorithm for Edge Detection

2020 ◽  
Vol 8 (6S) ◽  
pp. 11-17
Keyword(s):  
Edge Detection ◽  
Vital Role ◽  
Transition Model ◽  
Zero Crossing ◽  
Mean Intensity ◽  
Detection Algorithms ◽  
Grey Scale ◽  
A New Technique ◽  
Window Area ◽  
Over Time

Edge Detection plays a vital role in machine vision applications and thereby variety of edge detection algorithms being developed over time for both grey scale and colour images. In this paper, a new technique for edge detection called cumulative mean intensity differential transition algorithm (CuMIDT Algorithm) is proposed. This approach focuses on learning variations in the local pixel intensities and predicting the possible edge when the intensity deviation goes out of the stipulated window area. Ramps at the edge boundaries and zero crossing are addressed using differential transition model. Experimentation are done on standard FDDB dataset and real dataset. It is observed that the proposed approach gives better results when compared to the recently proposed novel edge detection algorithms.

scholarly journals COMPARATIVE ESTIMATION OF EDGE DETECTION ALGORITHMS ON EARTH SATELLITE IMAGES

2019 ◽  
Vol 1 (1) ◽  
pp. 150-158
Author(s):  
Baliar V.B. ◽  
◽  
Malashkin R.M. ◽  
Mazurkiewicz O.F.
Keyword(s):  
Edge Detection ◽  
Satellite Images ◽  
Earth Satellite ◽  
Detection Algorithms

scholarly journals Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

2020 ◽  
Vol 12 (2) ◽  
pp. 548 ◽  
Author(s):  
Romualdas Bausys ◽  
Giruta Kazakeviciute-Januskeviciene ◽  
Fausto Cavallaro ◽  
Ana Usovaite
Keyword(s):  
Edge Detection ◽  
Satellite Images ◽  
Satellite Image ◽  
Visual Features ◽  
Algorithm Selection ◽  
Adaptive Selection ◽  
Detection Algorithms ◽  
Product Assessment ◽  
Areas Of Interest ◽  
Selection Of

Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

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