scholarly journals Satellite image classification of Bangalore Urban and Bangalore Rural areas using Remote sensing and GIS Techniques.

2016 ◽  
Vol 4 (7) ◽  
pp. 2024-2048
Author(s):  
JagadeeshaMenappa Kattimani ◽  
◽  
T.J.Renuka Prasad. ◽  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Rural Areas ◽  
Satellite Image ◽  
Remote Sensing And Gis ◽  
Gis Techniques ◽  
Satellite Image Classification

scholarly journals A new approach to the application of conflict redistribution rule in Satellite Image Classification

2020 ◽  
pp. 12-16
Author(s):  
Sofiia Alpert
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Classification Methods ◽  
New Approach ◽  
Remote Sensing Classification ◽  
Satellite Image Classification ◽  
Bodies Of Evidence

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

scholarly journals SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH

2018 ◽  
Vol IV-2 ◽  
pp. 89-96 ◽  
Author(s):  
D. Duarte ◽  
F. Nex ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Binary Classification ◽  
Satellite Image ◽  
Classification Problem ◽  
Aerial Image ◽  
Management Service ◽  
Feature Maps ◽  
Satellite Image Classification

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

scholarly journals SOIL CLASSIFICATION OF BAHARIYA OASIS USING REMOTE SENSING AND GIS TECHNIQUES

2013 ◽  
Vol 4 (9) ◽  
pp. 921-947
Author(s):  
A. A. Elnaggar ◽  
Kh. H. El-Hamdi ◽  
A. B. A. Belal ◽  
M. M. El-Kafrawy
Keyword(s):  
Remote Sensing ◽  
Soil Classification ◽  
Remote Sensing And Gis ◽  
Gis Techniques ◽  
Bahariya Oasis

scholarly journals Object-Based and Rule-Based Classification of Synthetic Aperture Radar Images

2019 ◽  
Vol 8 (10) ◽  
pp. 2458-2463
Keyword(s):  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Accurate Information ◽  
Barren Land ◽  
Radar Images ◽  
Satellite Image Classification ◽  
Object Based ◽  
Terrain Features

Since thousands of years, the land is the basic and very important requirement for humans to survive and grow. The surface area of the earth provided by nature contains many different geographical locations divided into oceans, mountains, rivers, barren land, fertile land, ice caps and many more. The huge land masses and water bodies need to be observed and analyzed for optimum utilization of resources. Remote sensing is the best possible way to observe the earth's surface from a distance through different satellites and sensors. But most of the satellite images are not clear up to the extent to classify different terrain features accurately. Hence classification of image is needed to observe different terrain features in original images. In this study, the aim is to propose a branch of natural computation for SAR image classification into different terrain features with better information retrieval and accuracy measures as compared to traditional methods for satellite image classification. The object-based analysis has been used to extract spectral reflectance of five texture measures namely urban, rocky, vegetation, water and barren to generate training set. Minimum distance to mean classifier has been used with one of the Nature Inspired computation technique i.e. bacterial foraging optimization algorithm for the satellite image classification, to extract the more accurate information about land area of Alwar district, Rajasthan, India. In the proposed study a high-quality thematic map has been generated with the 7-band multi-spectral, medium-resolution satellite images. This approach provides the greater speed and accuracy in its computation with 97.43% overall accuracy (OA) and 0.96 Kappa co-efficient.

scholarly journals Satellite Image Classification for Environmental Analysis using Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 1513-1517
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Environmental Analysis ◽  
Supervised Classification ◽  
Satellite Image ◽  
Loss Of Life ◽  
Traditional Technique ◽  
Satellite Image Classification ◽  
The Future

Conventional supervised classification of satellite pictures utilizes a solitary multi-band picture and incidental ground perceptions to build phantom marks of land spread classes. We contrasted this methodology with three choices that get marks from different pictures and timespans. signature speculation, in this unearthly marks, is gotten from various pictures inside one season, however maybe from various years. signature extension, in this phantom marks, is made with information from pictures obtained during various periods of that year; and mixes of development and speculation. Utilizing the information for India, we evaluated the nature of these various marks to characterize the pictures used to infer the mark, and for use in transient mark expansion, i.e., applying a mark acquired from the information of one or quite a long while to pictures from different years. While applying marks to the pictures they were gotten from, signature development improved exactness comparative with the customary strategy, and inconstancy in precision declined uniquely. Conversely, signature speculation didn't improve grouping. While applying marks to pictures of different years (worldly expansion), the traditional technique, utilizing a mark got from a solitary picture, brought about extremely low characterization precision. Mark's development additionally performed ineffectively yet multi-year signature speculation performed much better and this seems, by all accounts, to be a promising methodology in the transient augmentation of ghastly marks for satellite picture arrangements. This project summarizes the different audits on satellite picture characterization strategies and systems. The summary helps the analysts to choose suitable satellite picture characterization strategies or methods dependent on the requirements. Later on, the results acquired from the proposed technique will be an extraordinary measure for anticipating and examining the effect of floods. It will help salvage groups to address high caution regions first in this way, least or no loss of life will be accomplished. In the future, the technique can be adjusted to be utilized for coastline location, urbanization, deforestation, and seismic tremors.

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