classification of remotely sensed data
Classification of Remotely Sensed Data by Texture Features with the Nature Inspire Optimization Algorithm
... Abstract: At regular intervals, the satellite remote sensing technologies collect images/data and volume of information gathered is substantial and it is developing exponentially as the innovation is developing at ...
8
Development and Evaluation of Advanced Classification Systems using Remotely Sensed Data for Accurate Land-Use/Land-Cover Mapping
... For each class, multiple image subsets for each class were first selected from the TM image using ERDAS IMAGINE tools. Our goal is not only to collect homogeneous image subsets but also to collect some image subsets from ...
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Linking remote sensing, land cover and disease
... supervised classification and so too its utility for epidemiological ...the classification (Campbell, 1996; Arora and Foody, 1997; Foody and Arora, ...the classification algorithm ...ancillary ...
50
Identification and Mapping of Riparian Vegetation in Eastern North Carolina by Remotely Sensed Data
... cover classification, to determine the filtering effects of the ...Automated classification techniques, supervised and unsupervised classification could be performed on digital imagery such as ...
104
Unsupervised classification of saturated areas using a time series of remotely sensed images
... Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or siting of management practices. However, in humid well vegetated climates ...
12
Automatic detection of lineaments from LANDSAT images in United Arab Emirate (UAE)
... image classification to perform geologic mapping or to locate spectrally anomalous zones attributable to mineralization (Mostafa et ...multiple data such as geological, geochemical, and geophysical ...
14
A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... image classification is the process of assigning land cover classes to ...Classifying remotely sensed data is a challenge because many factors such as complexity of landscape, image processing ...
10
Analysis of Change Detection Techniques using Remotely Sensed Data
... using remotely sensed Landsat multispectral ...post classification change detection technique provided the highest accuracy while the principal component analysis technique gave the least ...
6
Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data
... crop classification for high spatial reso- lution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still ...crop classification through satellite imagery in the ...
8
Rivers Hydromorphological Characterization from High Resolution Remotely Sensed Data
... This research aims first at the estimation of water fractions within the mixed pixels (i.e. unmixing) and then at the spatial allocation of corresponding sub-pixels (i.e. SRM) in order to map river boundaries at the ...
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A Feasibility Study on Applying Remotely Sensed data for Agricultural Management in Beyza Land
... these data, the image processing steps are implemented: image coding, atmospheric correction, feature extraction, image restoration and image enhancement, data fusion and image modeling and ...
5
Advances in Scene Classification of Remotely Sensed High Resolution Images and the Existing Datasets
... Developing a Improved Datasets: Almost all of the research on scene classification aims at improving the accuracy using the existing datasets. And we can say that it has reached a saturation level due to the ...
5
Eco-informatics modelling via semantic inference
... weather data collected from the urban Geographic Information System (GIS) systems and weather ...the data gathering instruments ...The data can be queried, reasoned over and/or have inference rules ...
28
Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data
... Model 2 (attributes shown in table 1) included only land surface temperature (LST) variables extracted from Land- sat TM data. Maximum, mean and range of LST were found to be statistically significant variables ...
13
Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu
... sensing data [14]. The availability of remotely sensed data from multiple dates enables us to carry out studies on urban modeling [8], urban landscape pattern analysis [15], and urban growth ...
23
Tropospheric ozone variability over Iraq as observed using remotely sensed data
... satellite data provided by AIRS on board NASA AQUA satellite has very high quality spectrally resolved radiance of varied atmospheric gases that used to identify sources and sinks of that gases, and detecting ...
6
Using the Opposition Effect in Remotely Sensed Data to Assist in the Retrieval of Bulk Density
... elevation data collected by G-LiHT were obtained from a field experiment in March 2015 at the Algodones Sand Dunes System [16, 61, 67, 68, ...(NAIP) data obtained from EarthExplorer ...spectral data ...
117
Using Remote Sensing Data for Yellowfin Tuna Fishing Ground Forecasting in Vietnamese Offshore Areas
... Temperature data is divided into two kinds of data including the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data and a real-time global sea surface temperature (SST) analysis by ...
8
Species and Ecosystems Stages of Biodiversity Evaluation Using Remotely-Sensed Data
... Knowledge of historical variations in landscapes offers helpful insights into present species and ecosystems biodiversity stages and supports future strategies on management, decision and conservation. The covers and ...
27
Development and Assessment of Advanced Data Fusion Algorithms for Remotely Sensed Data
... for data fusion is: “data fusion is a set of techniques used to generate fused images from a combination of primary images, by attempting to preserve the best characteristics of each primary image” ...
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