Multi-Scale Classification Based on Remote Sensing

2014 ◽  
Vol 580-583 ◽  
pp. 2853-2859
Author(s):  
Peng Li Li ◽  
Wei Ping Ti ◽  
Jia Chun Li
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Know How ◽  
Multi Scale ◽  
Single Scale ◽  
The Difference ◽  
Feature Values ◽  
Nearest Neighbour Classifier ◽  
Scale Classification

Due to the broadly application of remote sensing imagery, there is an eager need for the classification of objects in the images. The multi-scale classification based on object oriented analysis is not a usual approach for image classification because the users of multi-scale classification do not know how to use the information from multiple scales to do multi-scale classification. Many users rely on some easily accessible tools. nearest neighbour classifier, to do multi-scale classification. The multi-scale classification classifies the images from different scales. The feature values of the object vary from different scales and they may have some trends against scales. These trends may help us to understand multi-scale classification better. This is the scale dependency of features. The difference between multi-scale classification and single-scale classification is not only multiple scales, but also the use of information from different scales. In order to explore the connection between different scales, the research of new features is necessary.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

2016 ◽  
Vol XLI-B7 ◽  
pp. 517-522
Author(s):  
Yonghong Jia ◽  
Mingting Zhou ◽  
Ye Jinshan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Multi Scale ◽  
Single Scale ◽  
High Resolution Images ◽  
Image Object ◽  
Scale Classification

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

2016 ◽  
Vol XLI-B7 ◽  
pp. 517-522
Author(s):  
Yonghong Jia ◽  
Mingting Zhou ◽  
Ye Jinshan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Multi Scale ◽  
Single Scale ◽  
High Resolution Images ◽  
Image Object ◽  
Scale Classification

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

scholarly journals Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Field Survey ◽  
Critical Level ◽  
Satellite Image ◽  
Geographic Information ◽  
Level Data ◽  
The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

scholarly journals APLIKASI SIG UNTUK PENETAPAN KESESUAIAN WILAYAH PENERAPAN MEKANISASI PERTANIAN TANAMAN PANGAN

2016 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Mubekti Mubekti
Keyword(s):  
Remote Sensing ◽  
Limiting Factors ◽  
Limiting Factor ◽  
Physical Parameters ◽  
Know How ◽  
Agricultural Mechanization ◽  
Preliminary Research

The study is the preliminary research aiming to apply the usefulness of the GISsystem for classifying suitability of region to the agricultural mechanization. Simplebasic theory of GIS and classification of agricultural mechanization were presented.Nine physical and non-physical parameters as limiting factors were defined and aweight of each parameter was calculated in order to know how far its effect to theagricultural mechanization practice. Data coming from several types, ie: numeric,vector and remote sensing (raster) were prepared to support the process. Twoordoes consisting of five classes were employed to extract suitability classificationof each District in Jawa Barat dan Banten Province. The results show that tendistricts are classified into suitable for applying agricultural mechanization, and therest ten districts are classified into not suitable. Depending on the dynamic characterof limiting factor, suitability class would possibly change along the time. Saveralconclusions and advices for improving research are given.

scholarly journals Multi-scale classification of fluvial architecture: An example from the Palaeocene-Eocene Bighorn Basin, Wyoming

Sedimentology ◽  
2017 ◽  
Vol 64 (6) ◽  
pp. 1572-1596 ◽  
Author(s):  
Amanda Owen ◽  
Alena Ebinghaus ◽  
Adrian J. Hartley ◽  
Maurício G. M. Santos ◽  
Gary S. Weissmann
Keyword(s):  
Bighorn Basin ◽  
Multi Scale ◽  
Fluvial Architecture ◽  
Scale Classification

ARTMAP Neural Networks for Multispectral Image Classification

2000 ◽  
Vol 4 (4) ◽  
pp. 240-245
Author(s):  
Norbert Kopco ◽  
◽  
Peter Sincak ◽  
Stanislav Kaleta ◽  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Feature Space ◽  
Fuzzy Artmap ◽  
Cluster Type ◽  
The Difference ◽  
Analysis Of Performance

This paper presents an analysis of performance of several types of the ARTMAP neural network. The performance of the networks is analyzed in the task of classification of satellite images obtained by remote sensing. The analysis is concentrated on the dependence of classification accuracy on the difference in cluster type preferably identified by each of the classifiers. Three types of ARTMAP classifier are compared: fuzzy ARTMAP, Gaussian ARTMAP, and Extended Gaussian ARTMAP The main difference among these classifiers is in the way they determine/represent individual clusters in feature space. Best results are obtained for Extended Gaussian ARTMAP, a modification of the Gaussian ARTMAP neural network that preferably identifies Gaussian-distributed clusters.

Multi-scale classification of disease using structural MRI and wavelet transform

NeuroImage ◽  
2012 ◽  
Vol 62 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Kerstin Hackmack ◽  
Friedemann Paul ◽  
Martin Weygandt ◽  
Carsten Allefeld ◽  
John-Dylan Haynes
Keyword(s):  
Wavelet Transform ◽  
Structural Mri ◽  
Multi Scale ◽  
Scale Classification

scholarly journals CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES

2016 ◽  
Vol III-3 ◽  
pp. 169-176 ◽  
Author(s):  
R. Blomley ◽  
B. Jutzi ◽  
M. Weinmann
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Benchmark Dataset ◽  
Airborne Laser Scanning ◽  
Geometric Features ◽  
Multi Scale ◽  
Airborne Laser ◽  
Single Scale ◽  
Detailed Evaluation

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

scholarly journals Efficient feature for classification of eye movements using electrooculography signals

2016 ◽  
Vol 20 (suppl. 2) ◽  
pp. 563-572 ◽  
Author(s):  
Pornchai Phukpattaranont ◽  
Siriwadee Aungsakul ◽  
Angkoon Phinyomark ◽  
Chusak Limsakul
Keyword(s):  
Eye Movements ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Scatter Plot ◽  
Mean Values ◽  
Maximum Peak ◽  
The Mean ◽  
The Difference ◽  
Feature Values

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

scholarly journals Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

2020 ◽  
Vol 12 (5) ◽  
pp. 872 ◽  
Author(s):  
Ronghua Shang ◽  
Jiyu Zhang ◽  
Licheng Jiao ◽  
Yangyang Li ◽  
Naresh Marturi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Remote Sensing Images ◽  
Global Features ◽  
Global Average ◽  
Multi Scale ◽  
Context Extraction

Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.

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