710 research outputs found
A review of remotely sensed satellite image classification
Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies
Urban scene description for a multi scale classication of high resolution imagery case of Cape Town urban Scene
Includes abstract.Includes bibliographical references.In this paper, a multi level contextual classification approach of the City of Cape Town, South Africa is presented. The methodology developed to identify the different objects using the multi level contextual technique comprised three important phases
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators
A recent interest in resting state functional magnetic resonance imaging
(rsfMRI) lies in subdividing the human brain into anatomically and functionally
distinct regions of interest. For example, brain parcellation is often used for
defining the network nodes in connectivity studies. While inference has
traditionally been performed on group-level data, there is a growing interest
in parcellating single subject data. However, this is difficult due to the low
signal-to-noise ratio of rsfMRI data, combined with typically short scan
lengths. A large number of brain parcellation approaches employ clustering,
which begins with a measure of similarity or distance between voxels. The goal
of this work is to improve the reproducibility of single-subject parcellation
using shrinkage estimators of such measures, allowing the noisy
subject-specific estimator to "borrow strength" in a principled manner from a
larger population of subjects. We present several empirical Bayes shrinkage
estimators and outline methods for shrinkage when multiple scans are not
available for each subject. We perform shrinkage on raw intervoxel correlation
estimates and use both raw and shrinkage estimates to produce parcellations by
performing clustering on the voxels. Our proposed method is agnostic to the
choice of clustering method and can be used as a pre-processing step for any
clustering algorithm. Using two datasets---a simulated dataset where the true
parcellation is known and is subject-specific and a test-retest dataset
consisting of two 7-minute rsfMRI scans from 20 subjects---we show that
parcellations produced from shrinkage correlation estimates have higher
reliability and validity than those produced from raw estimates. Application to
test-retest data shows that using shrinkage estimators increases the
reproducibility of subject-specific parcellations of the motor cortex by up to
30%.Comment: body 21 pages, 11 figure
Image Automatic Categorisation using Selected Features Attained from Integrated Non-Subsampled Contourlet with Multiphase Level Sets
A framework of automatic detection and categorization of Breast Cancer (BC) biopsy images utilizing significant interpretable features is initially considered in discussed work. Appropriate efficient techniques are engaged in layout steps of the discussed framework. Different steps include 1.To emphasize the edge particulars of tissue structure; the distinguished Non-Subsampled Contourlet (NSC) transform is implemented. 2. For the demarcation of cells from background, k-means, Adaptive Size Marker Controlled Watershed, two proposed integrated methodologies were discussed. Proposed Method-II, an integrated approach of NSC and Multiphase Level Sets is preferred to other segmentation practices as it proves better performance 3. In feature extraction phase, extracted 13 shape morphology, 33 textural (includes 6 histogram, 22 Haralick’s, 3 Tamura’s, 2 Graylevel Run-Length Matrix,) and 2 intensity features from partitioned tissue images for 96 trained image
The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments
Soil is an important non-renewable source. Its protection and allocation is critical to
sustainable development goals. Urban development presents an important drive of soil
loss due to sealing over by buildings, pavements and transport infrastructure.
Monitoring sealed soil surfaces in urban environments is gaining increasing interest
not only for scientific research studies but also for local planning and national
authorities.
The aim of this research was to investigate the extent to which automated classification
methods can detect soil sealing in UK urban environments, by remote sensing. The
objectives include development of object-based classification methods, using two
types of earth observation data, and evaluation by comparison with manual aerial
photo interpretation techniques.
Four sample areas within the city of Cambridge were used for the development of an
object-based classification model. The acquired data was a true-colour aerial
photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral
resolution). The classification scheme included the following land cover classes: sealed
surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also
identified as an initial class and attempts were made to reclassify them into the actual
land cover type. The accuracy of the thematic maps was determined by comparison
with polygons derived from manual air-photo interpretation; the average overall
accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces
resulted in a statistically significant accuracy increase to 92%. The integration of
ancillary data (OS MasterMap) into the object-based model did not improve the
performance of the model (overall accuracy of 91%). The use of satellite data in the
object-based model gave an overall accuracy of 80%, a 7% decrease compared to the
aerial photography.
Future investigation will explore whether the integration of elevation data will aid to
discriminate features such as trees from other vegetation types. The use of colour
infrared aerial photography should also be tested. Finally, the application of the object-
based classification model into a different study area would test its transferability
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