A case study on an improved method for very high spatial resolution satellite image classification: watersheds across the complex environment of high Pacific islands

2008 ◽  
Author(s):  
Marc Despinoy ◽  
Maelle Aubert ◽  
Mickael Barotin ◽  
Morgan Mangeas
Keyword(s):  
Image Classification ◽  
Spatial Resolution ◽  
Pacific Islands ◽  
High Spatial Resolution ◽  
Satellite Image ◽  
Complex Environment ◽  
Improved Method ◽  
Very High Spatial Resolution ◽  
Very High

scholarly journals Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

2017 ◽  
Author(s):  
Zhiyong Lv ◽  
Penglin Zhang ◽  
Jón Atli Benediktsson
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Aerial Imagery ◽  
Parameter Determination ◽  
Vector Data ◽  
Spatial Feature ◽  
Very High Spatial Resolution ◽  
Very High

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

scholarly journals Analysis of Very High Spatial Resolution Images for Automatic Shoreline Extraction and Satellite-Derived Bathymetry Mapping

Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 172
Author(s):  
Giovanni Randazzo ◽  
Giovanni Barreca ◽  
Maria Cascio ◽  
Antonio Crupi ◽  
Marco Fontana ◽  
...  
Keyword(s):  
Shallow Water ◽  
Spatial Resolution ◽  
Water Depth ◽  
High Spatial Resolution ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Coastal Areas ◽  
Ratio Method ◽  
Very High Spatial Resolution ◽  
Very High

The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world’s most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R2 = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas.

Sign in / Sign up

Export Citation Format

Share Document