Knowledge-based interpretation of satellite data by object-based and multi-scale image analysis in the context of nuclear verification

2002 ◽  
Author(s):  
I. Niemeyer ◽  
M.J. Canty
Keyword(s):  
Image Analysis ◽  
Satellite Data ◽  
Multi Scale ◽  
Knowledge Based ◽  
Object Based

scholarly journals A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 241-247 ◽  
Author(s):  
T. Kavzoglu ◽  
M. Yildiz Erdemir ◽  
H. Tonbul
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Classification Accuracy ◽  
Scale Parameter ◽  
Rate Of Change ◽  
Multi Scale ◽  
Object Based Image Analysis ◽  
Optimal Scale ◽  
Segmentation Process ◽  
Object Based

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

scholarly journals Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan

2011 ◽  
Vol 11 (10) ◽  
pp. 2715-2726 ◽  
Author(s):  
T. Lahousse ◽  
K. T. Chang ◽  
Y. H. Lin
Keyword(s):  
Image Analysis ◽  
Spectral Characteristics ◽  
Satellite Image ◽  
Shallow Landslides ◽  
Training Dataset ◽  
Validation Dataset ◽  
Spectral Difference ◽  
Multi Scale ◽  
Object Based Image Analysis ◽  
Object Based

Abstract. We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m2. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.

A Multi Scale Feature Extraction Approach to Improve Land Use Land Cover Classification Accuracy using IRS LISS IV Imagery

2016 ◽  
Vol 1 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Kuntal Ganguly ◽  
Mohit Modi ◽  
Manoj Raj Saxena ◽  
Ravali Bharadwaj ◽  
Divya Vijayan V. ◽  
...  
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Land Cover ◽  
Large Scale ◽  
Land Use Land Cover ◽  
Scale Feature ◽  
Multi Scale ◽  
Knowledge Based ◽  
Object Based ◽  
Processing Techniques

The study presents an approach to map Land Use / Land Cover Change (LULCC) at large scale and processing techniques that permit higher accuracy. IRS RESOURCESAT-2 LISS-IV images of Nellore district of Andhra Pradesh were used to apply the classification technique. In multi-scale feature extraction approach LULCC takes two forms i.e. conversion from one category of LULCC to another and modification of condition within a category. Thus, major LULCC classes were extracted using object based approach and uncertain classes were identified using onscreen knowledge based method. The results showed in 2009, the accuracy of cropland, water body and built-up segments were 99.3%, 94.79% and 89.72%, respectively, whereas, in 2013 the accuracies were 94.31%, 88.26% and 81.20%, respectively. Hence, this classification approach can be useful in different landscape structure over the time, which can be quantified and assessed to achieve a better understanding of the land cover.

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