scholarly journals A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yingxia Chen ◽  
Guixu Zhang
Keyword(s):  
Performance Metrics ◽  
Spatial Information ◽  
Evolutionary Optimization ◽  
Transformation Model ◽  
New Approach ◽  
Sensing Applications ◽  
High Quality Image ◽  
Fused Image ◽  
Remote Sensing Applications ◽  
A New Technique

In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan-sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity-hue-saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state-of-the-art techniques according to the performance metrics.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Multi-focus Image Fusion Using Hybrid Transformation

2018 ◽  
pp. 352-359
Author(s):  
Dr. Sheshang D. Degadwala ◽  
Arpana Mahajan ◽  
Dhairya Vyas ◽  
Shivam Upadhyay ◽  
Harsh S Dave
Keyword(s):  
Image Fusion ◽  
Transform Domain ◽  
The Third ◽  
Sensing Applications ◽  
Fused Image ◽  
Focus Image ◽  
Speed Up ◽  
Remote Sensing Applications ◽  
Real Time Applications ◽  
Surveillance Application

The technique of blending two images or more than two images which produces outcome as the composite fused image. The obtained fused image is the upgraded version of original images because it has all the salient information. The present applications makes majority usage of this fused image to speed up their processing tasks in their respective fields. Recent real-time applications which require image fusion are remote sensing applications, medical applications, surveillance application, photography applications etc. the broad categorization of image fusion techniques are Non-transform domain or spatial domain and Transform domain or frequency domain. This paper initiates with the introduction of image fusion. In the second section it explains the analysis of multi-focus techniques. The third section explains hybrid image fusion strategy. Further sections elaborates the taxonomy of image fusion techniques and their comparative analysis with results.

scholarly journals Geo Information Systems &Remote Sensing: Applications in Environmental Systems & Management

2020 ◽  
pp. 11-18
Author(s):  
P.K. Paul ◽  
P. S. Aithal ◽  
A. Bhuimali ◽  
K.S. Tiwary ◽  
R. Saavedra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Information Science ◽  
Spatial Information ◽  
Physical Content ◽  
Gis And Remote Sensing ◽  
Interdisciplinary Field ◽  
Sensing Applications ◽  
Database Technology ◽  
Remote Sensing Applications

Geo Informatics is an interdisciplinary field responsible for spatial information related activities. Geo Informatics is close to the Geo Information Science, Geo Information System, Remote Sensing, etc. Geo Informatics is a combination of Geo Science and Information Science and here different kinds of IT and Computing tools are being used such as Database Technology, Network Technology, Web Technology, Multimedia Technology, etc in the spatial data management. Remote Sensing is considered as a component of Geo Information Science dedicated in gathering of information on the different types of objects without physical content and applicable in different areas of the geography, survey of land and different type of geo related areas viz. Hydrology, Ecology, Meteorology, Oceanography and Geology, etc. The term remote sensing is also called as GIS & RS due to their relationship and their importance. The applications of the IT in Geography and allied areas are called as Geo Informatics or Geo Information Science. Similarly, the applications and utilization of IT, Information Science and Computing in Environment and allied areas are known as Environmental Informatics or Environmental Information Science. The GIS and Remote Sensing applications in the environment and ecological areas are increasing rapidly and it includes various existing and emerging applications. This paper talks about the applications of the GIS and RS in Environmental Applications and Management.

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

scholarly journals A Soft Shadow Detection Method Based on MRF for Remote Sensing Images

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Pengwei Li ◽  
Wenying Ge
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Detection Method ◽  
Spectral Characteristics ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Soft Shadow ◽  
Remote Sensing Applications

Shadows limit many remote sensing applications such as classification, target detection, and change detection. Most current shadow detection methods utilize the histogram threshold of spectral characteristics to distinguish the shadows and nonshadows directly, called “hard binary shadow.” Obviously, the performance of threshold-based methods heavily rely on the selected threshold. Simultaneously, these threshold-based methods do not take any spatial information into account. To overcome these shortcomings, a soft shadow description method is developed by introducing the concept of opacity into shadow detection, and MRF-based shadow detection method is proposed in order to make use of neighborhood information. Experiments on remote sensing images have shown that the proposed method can obtain more accurate detection results.

scholarly journals Optimizing Sensor Relationship for Fusion of Multispectral and Panchromatic Imagery

2019 ◽  
Vol 9 (2) ◽  
pp. 3077-3083
Keyword(s):  
Remote Sensing ◽  
Optimization Approach ◽  
Modulation Transfer ◽  
Sensing Applications ◽  
Fused Image ◽  
Satellite Sensors ◽  
Assessment Techniques ◽  
Remote Sensing Applications ◽  
Imaging Sensors ◽  
Relationship Of

The remote sensing satellite products: multispectral and panchromatic imagery are characterized by different levels of spatio-spectral resolutions. The fusion of these two images (provided, they are acquired for same geographic scenario) is also known as ‘Pansharpening’. This produces a composite image featuring simultaneous high levels of spatio-spectral resolutions to meet the demand of the most of remote sensing applications. Thus, different approaches for such fusion and further its quality assessment are continuously researched. The modulation transfer function is unique to the imaging sensors. In this paper, the sensor relationship of the input imagery is optimized to produce the efficient pansharpened/fused image. The performance measurement is carried out on two real datasets made available by WorldView-2 and WorldView-3 satellite sensors using two assessment techniques. The results of optimization approach are further compared to nine different most recent fusion algorithms

Design and Development of wide beam width Antenna for Ionosphere Wireless Remote Sensing Applications

2020 ◽  
Vol 10 ◽  
Author(s):  
M. Durga Rao ◽  
I. Srinivasa Rao
Keyword(s):  
Remote Sensing ◽  
Reflection Coefficient ◽  
Radiation Pattern ◽  
Beam Width ◽  
New Approach ◽  
Wide Beam ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Simulation Results ◽  
Ionospheric Radar

Background: The Yagi-Uda antenna is a highly directive antenna used widely in many applications including pulsed Doppler radars to study the dynamics of the atmosphere. Yagi antennas configured in planar array configurations in phased array radars to achieve high peak powers to probe the atmosphere from troposphere. In this paper, a twoelement Yagi-Uda antenna design is presented to investigate the ionospheric irregularities from the Gadanki Ionospheric Radar Interferometer. A new approach devised for the first time to design the two element, wide beam width tilted Yagi antenna, where folded dipole acts as active driver element and reflector as parasitic element. Methods: Several design techniques have been studied and new approach has been employed in designing the antenna and simulations have been carriedout and optimized the performance at 30 MHz with 14o tilt towards geometric north from vertical (zenith) direction for the maximum back scattered echo gain. Based on the design antenna has been fabricated and the system performance has been evaluated. Detailed validation methods have been listed to validate the parameters like reflection coefficient, gain, bandwidth and front-to-back ratio. Results: The antenna is designed and simulated results with 4NEC2 provided the optimized parameters before fabrication. The measured results indicate that the antenna has a gain of 5.65dBi and a reflection coefficient of -30 dB and these results are in close agreement with the simulation results. The band width obtained is about 2MHz is very good for the ionospheric remote sensing applications. The peak power handling capability upto 1kW shows the reliable system design for continuous and long term use of the system. Conclusion: Two element wide beam width 14o tilted Yagi-Uda antenna at 30MHz has been designed, simulated and optimized. Realized system performance validated to use for ionospheric radar remote sensing application. Details of the test methodologies are explained and the same have been executed to characterize the performance of the fabricated antenna with simulation results by measuring reflection coefficient, gain, radiation pattern. All the measured results have very close agreement with the simulation results and satisfy the design requirements to fit into 30 MHz radar antenna array for dedicated ionospheric probing. In future, we intended to carry out the radiation pattern simulation of the 20x8 phased array antennas to describe the overall radiation pattern.

A Two-Stage PAN-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction

2021 ◽  
pp. 2250007
Author(s):  
Rajesh Gogineni ◽  
Dhara J Sangani
Keyword(s):  
Sparse Representation ◽  
Spatial Information ◽  
Spectral Information ◽  
Technological Advancement ◽  
Modulation Transfer ◽  
Two Stage ◽  
Sensing Applications ◽  
Fused Image ◽  
Iterative Filtering ◽  
Distortion Reduction

Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pléiades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details.

scholarly journals Approaches for Hyperspectral Image Classification Detailed Review

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Kushalatha M R ◽  
◽  
Prasantha H S ◽  
Beena R. Shetty ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Hyperspectral Image Classification ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

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