Mr. Amirhoushang Ehsani Profile

Amirhoushang Ehsani

at Kungliga Tekniska Högskolan

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 29 October 2007 Paper

Comparison of multi-resolution SRTM data for morphometric features identification using neural network self-organizing map (case study: Eastern Carpathians)

Amirhoushang Ehsani, Friedrich Quiel

Proceedings Volume 6749, 67491J (2007) https://doi.org/10.1117/12.737857

KEYWORDS: Neural networks, Spatial resolution, Quantization, Landsat, Evolutionary algorithms, Statistical analysis, Inspection, Visualization, Zoom lenses, Earth observing sensors

Read Abstract +

The Shuttle Radar Topography Mission (SRTM) was launched on 11 February 2000 and 3 arc second data were publicly released in July 2004. Easy availability of SRTM 3 arc second data, covering almost 80% of the land surface on earth, has resulted in great advances in morphometric studies and numerical description of landscape features. In this study we introduce a new procedure using Neural Network - Self Organizing Map - to characterize morphometric features of landscapes.. We also investigate the effect of two resolutions for morphometric feature identification. Specifically we investigate how the SRTM 3arc second latitude / longitude data projected to UTM coordinates with 90 meter respectively 28.5 m grid, corresponding to Landsat TM data resolution, affect the morphometric characterization. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 for the 90 m data (450 m on the ground) and 9×9 for the 28.5 m data (about 250 m) . Kohonen Self Organizing Map as an unsupervised neural network algorithm is employed for the classification of these morphometric parameters into 10 exclusive and exhaustive classes. These classes were analyzed and interpreted as morphometric features such as ridge, channel, crest line, planar and valley bottom for both data sets based on morphometric signatures, feature space and 3D inspection of the area. The difference change detection technique was used between two DEMs (DEM-90 and DEM-28.5 m) to analyze differences in morphometric features identification. The results showed that the introduced method is very useful for identification of morphometric features. Increasing spatial resolution from 90 meter to 28.5 meter, can produce digital elevation models (DEMs) allowing more precise identification of morphometric features and landforms. Increasing spatial resolution overcomes the main constrains for morphometric analysis with SRTM 90 m data, such as artifacts, unrealistic feature presentations and isolated single elements in the output map. Increased spatial resolution together with the smaller window size emphasized local conditions but main morphometric features were preserved. An overall change of 66.36 % is observed for morphometric features in the 28.5 meter DEM. The most and least frequent changes occurred for class no.6 (moderate slopes, channel) with 82.74% and class no.7 (Gentle slope to flat, valley bottom, planar) with 43.31% respectively. Increasing spatial resolution can be applied also to watersheds studies like drainage modeling.

Proceedings Article | 26 October 2007 Paper

Contribution of Landsat ETM+ thermal band to land cover classification using SMAP and ML algorithms (case study: Eastern Carpathians)

Amir Houshang Ehsani, Friedrich Quiel

Proceedings Volume 6748, 67481F (2007) https://doi.org/10.1117/12.737841

KEYWORDS: Landsat, Reflectivity, Agriculture, Image segmentation, Image classification, Image processing algorithms and systems, Remote sensing, Principal component analysis, Data acquisition, Earth observing sensors

Read Abstract +

Landsat Thermal band measures the emitted radiation of the earth surface. In many studies the ETM+ thermal band with 60 meter resolution is excluded from processing and classification despite the valuable information content. Two different methods of Bayesian segmentation algorithm were used with different band combinations. Sequential Maximum a Posteriori (SMAP) is a Bayesian image segmentation algorithm which unlike the traditional Maximum likelihood (ML) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. Landsat 7 ETM+ data with Path/Row 186-26, dated 30 September 2000 were used. In order to study the role of thermal band with these methods, two data sets with and without the thermal band were used. Nine band combinations including ETM+ and Principal Component (PC) data were selected based on the highest value of Optimum Index Factor (OIF). Using visual and digital analysis, field observation data and auxiliary map data like CORINE land cover, 14 land cover classes are identified. Spectral signatures were derived for every land cover. Spectral signatures as well as feature space analysis were used for detailed analysis of efficiency of the reflective and thermal bands. The result shows that SMAP as the superior method can improve Kappa values compared with ML algorithm for all band combinations with on average 17%. Using all 7 bands both SMAP and ML classifications algorithm achieved the highest Kappa accuracy of 80.37 % and 64.36 % respectively. Eliminating the thermal band decreased the Kappa values by about 8% for both algorithms. The band combination including PC1, 2, 3, and 4 (PCA calculated for all 7 bands) produced the same Kappa as bands 3, 4, 5 and 6. The Kappa value for band combination 3, 4, 5 and 6 was also about 4% higher than using 6 bands without the thermal band for both algorithms. Contextual classification algorithm like SMAP can significantly improve classification results. The thermal band bears complementary information to other spectral bands and despite the lower spatial resolution improves classification accuracy.

Proceedings Article | 24 October 2007 Paper

Landforms identification using neural network self organizing map and SRTM data (case study: Eastern Carpathians)

Amir Houshang Ehsani, Friedrich Quiel

Proceedings Volume 6748, 67481H (2007) https://doi.org/10.1117/12.737949

KEYWORDS: Volume rendering, Neural networks, Brain mapping, Zoom lenses, Radar, Feature extraction, Neurons, Quantization, Evolutionary algorithms, Data acquisition

Read Abstract +

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales. In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish - Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis. Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines. Results showed that Self Organizing Map is a very promising and efficient tool for such studies. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years