scholarly journals A MULTI THREADED FEATURE EXTRACTION TOOL FOR SAR IMAGES USING OPEN SOURCE SOFTWARE LIBRARIES

2018 ◽  
Vol XLII-5 ◽  
pp. 155-160
Author(s):  
C. Bipin ◽  
C. V. Rao ◽  
P. V. Sridevi ◽  
S. Jayabharathi ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Computational Complexity ◽  
Remote Sensing Data ◽  
Low Complexity ◽  
Higher Order ◽  
Sar Images ◽  
Automatic Feature Extraction ◽  
Software Libraries ◽  
Computationally Intensive

<p><strong>Abstract.</strong> In this paper, we propose a software architecture for a feature extraction tool which is suitable for automatic extraction of sparse features from large remote sensing data capable of using higher order algorithms (computational complexity greater than <i>O</i>(<i>n</i>)). Many features like roads, water bodies, buildings etc in remote-sensing data are sparse in nature. Remote-sensing deals with a large volume of data usually not manageable fully in the primary memory of typical workstations. For these reason algorithms with higher computational complexity is not used for feature extraction from remote sensing images. A good number of remote sensing applications algorithms are based on formulating a representative index typically using a kernel function which is having linear or less computational complexity (less than or equal to <i>O</i>(<i>n</i>)). This approach makes it possible to complete the operation in deterministic time and memory.</p><p>Feature extraction from Synthetic Aparture Radar (SAR) images requires more computationally intensive algorithm due to less spectral information and high noise. Higher Order algorithms like Fast Fourier Transform (FFT), Gray Level Co-Occurrence Matrix (GLCM), wavelet, curvelet etc based algorithms are not preferred in automatic feature extraction from remote sensing images due to their higher order of computational complexity. They are often used in small subsets or in association with a database where location and maximum extent of the features are stored beforehand. In this case, only characterization of the feature is carried out in the data.</p><p>In this paper, we demonstrate a system architecture that can overcome the shortcomings of both these approaches in a multi-threaded platform. The feature extraction problem is divided into a low complexity with less accuracy followed by a computationally complex algorithm in an augmented space. The sparse nature of features gives the flexibility to evaluate features in Region Of Interest (ROI)s. Each operation is carried out in multiple threads to minimize the latency of the algorithm. The computationally intensive algorithm evaluates on a ROI provided by the low complexity operation. The system also decouples complex operations using multi-threading.</p><p>The system is a customized solution developed completely in python using different open source software libraries. This approach has made it possible to carry out automatic feature extraction from Large SAR data. The architecture was tested and found giving promising results for extraction of inland water layers and dark features in ocean surface from SAR data.</p>

Approaches to Comparison and Their Influence on Social Comparison

2019 ◽  
pp. 430-442
Author(s):  
Arthur B. Markman
Keyword(s):  
Cognitive Psychology ◽  
Computational Complexity ◽  
Social Comparison ◽  
Mental Representation ◽  
Spatial Models ◽  
Structural Models ◽  
Low Complexity ◽  
Comparison Process ◽  
Social Settings ◽  
Computationally Intensive

Cognitive psychology identifies different assumptions about the mental representations that form the basis of theories of comparison. Each representation requires a different process to generate a comparison, and both the computational complexity and the output of the different processes differ. Spatial models require a low-complexity process but only reveal the distance between points representing individuals. Featural models are more intensive than spatial comparisons but provide access to particular commonalities and differences. Structural models are more computationally intensive but support a distinction between alignable and nonalignable differences. Social comparison theories make assumptions about how knowledge is represented, but they are rarely explicit about the type of comparison process that is likely to be involved. The merging of work on social comparison with more explicit cognitive science theories of comparison science has the potential to both identify gaps in the literature and expand our knowledge about how comparison operates in social settings. This chapter first discusses the concept of mental representation and then addresses spatial models of comparison, featured models of comparison, structural models of comparison, transformation models. The chapter concludes with a discussion of similarity models and social comparison.

Feature extraction of hyperspectral remote sensing data using supervised neighbor reconstruction analysis

2016 ◽  
Vol 45 (10) ◽  
pp. 1028003
Author(s):  
方 敏 Fang Min ◽  
王 君 Wang Jun ◽  
王红艳 Wang Hongyan ◽  
李天涯 Li Tianya
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Editorial to Special Issue “Remote Sensing Data Compression”

2021 ◽  
Vol 13 (18) ◽  
pp. 3727
Author(s):  
Benoit Vozel ◽  
Vladimir Lukin ◽  
Joan Serra-Sagristà
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Data Compression ◽  
Remote Sensing Data ◽  
Low Complexity ◽  
Special Issue ◽  
Huge Amount ◽  
Sensing Data ◽  
The Impact ◽  
Lossless And Lossy Compression

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

Circulant Matrix-Based Continuous Wavelet Transform for Achieving Low Complexity Electrocardiogram Feature Extraction in Health Monitoring Applications

2020 ◽  
Vol 17 (1) ◽  
pp. 254-259
Author(s):  
Harikrishna Ponnam ◽  
Jakeer Hussain Shaik
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Computational Complexity ◽  
Error Rate ◽  
Continuous Wavelet Transform ◽  
Low Complexity ◽  
Circulant Matrix ◽  
Continuous Wavelet ◽  
Ecg Signals ◽  
Gradient Based

In the application of remote cardiovascular monitoring, the computational complexity and power consumption need to be maintained in a considerable level in order to prevent the limitations introduced by the computationally constrained equipment’s that perform the process of continuous monitoring and analysis. In this paper, a Circulant Matrix-based Continuous Wavelet Transform (CM-CWT)-based feature extraction mechanism is contributed to minimizing the computational complexity incurred during the process of feature extraction from the input ECG signals. This proposed CM-CWT mechanism derives the advantages of the Circulant Matrix-based Continuous Wavelet Transform and Gradient-based filtering design for achieving excellent feature extraction from ECG signals with low computational complexity. The experimental investigation of the proposed CM-CWT mechanism is conducted using the factors of computational complexity, sensitivity, prediction accuracy and error rate for estimating its predominance over the compared DWT-HAAR and HIFEA approaches used for ECG feature extraction. The experiments of the proposed CM-CWT mechanism on an average is estimated to reduce the error rate to the maximum of 21% compared to the existing DWT-HAAR and HIFEA approaches used for ECG feature extraction.

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