Object Based Classification of Multi temporal Images for Agricultural Crop Mapping in Karacabey Plain Turkey

Download
2014-01-01
ÖZDARICI OK, ASLI
Akyürek, Sevda Zuhal
The objective of this research is to classify major crop types cultivated in Karacabey Plain of north western Turkey using multitemporal Kompsat-2 and Envisat ASAR data with an object-based methodology. First a pansharpening algorithm is applied to each panchromatic and multispectral Kompsat-2 data to produce colour images having 1m spatial resolution. Next, Mean-Shift image segmentation procedure is applied to the pansharpened Kompsat-2 data with multiple parameter combinations. Multiple goodness measures are utilized to evaluate the object-based results. The optimum objects are then employed in object-based classifications of the single-date images. Next, single-date multispectral (MS) Kompsat-2 images and Kompsat-2 images along with the Envisat ASAR data are classified with the Support Vector Machines (SVMs) method. The training samples are provided automatically by the selected objects based on spatial statistical properties. Next, probability maps are generated for each image in pixel-based manner during the image classification operations. The maximum probabilities are then assigned to the pixels as class labels and the combined thematic maps (June-July, June-August, June-July-August) are generated in pixel-based and object-based manners. The produced thematic maps are evaluated through the confusion matrices and compared also with the results of parcel-based classifications using original agricultural parcels. Results indicate that the combined thematic maps of June-August and June-JulyAugust provide the highest overall accuracy and kappa value approximately 92% and 0.90, respectively.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Suggestions

A multi-temporal masking classification method for field-based agricultural crop mapping
Arıkan, Mahmut; Türker, Mustafa; Department of Geodetic and Geographical Information Technologies (2003)
This study describes the field-based classification of agricultural crops using multi-date Landsat 7 ETM+ images acquired in May, July, and August 2000. The study area is located in north-west of Turkey with a size of about 1 5 km x 1 1.3 km and grows a variety of crops. The objective was to identify the summer (August) crops within the agricultural fields. The classification methodology is based on a multi- temporal masking of Landsat 7 ETM+ images. First, a supervised per-pixel classification of the three...
Integration of Surface Wave Seismic Survey Testing Results and Segmented Seismic Source Model of NAFS for PSHA of the Gölyaka Düzce Tectonic Basin Turkey(2005)
Yousefibavil, Karim; Akgün, Haluk; Eker, Arif Mert; Cambazoğlu, Selim; Koçkar, Mustafa Kerem (null; 2015-11-04)
This study mainly focuses on the determination of site effects for the Plio-Quaternary sediments of Gölyaka-Düzce, Turkey by characterizing local soil conditions. Probabilistic seismic hazard assessment (PSHA) analysis was performed to observe the effect and importance of local site conditions. The study area is located within the Eastern Marmara Region and uniquely falls within the bifurcated section of the North Anatolian Fault System (NAFS). This tectonic basin is bounded by the surface rupture of the 19...
A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data
Ok, Asli Ozdarici; Akyürek, Sevda Zuhal (2012-01-01)
This research study aims to classify crop diversity in agricultural land with a segment-based approach using multi-temporal Kompsat-2 and Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) data acquired in June, July and August on Karacabey Plain, Turkey. Analyses start with the image segmentation process applied to the fused optical images to search homogenous objects. The segmentation outputs are evaluated using multiple goodness measures, which take into consideration area and loc...
Application of artificial neural network and logistic regression methods to landslide susceptibility mapping and comparison of the results for the ulus district, Bartin Bartin, ulus ilçesi için yapay sinir aǧi ve lojistik regresyon yöntemlerinin heyelan duyarlilik çalişmasina uygulanmasi ve karşilaştirilmasi
Eker, Arif Mert; Dikmen, Mehmet; Cambazoǧlu, Selim; Düzgün, Şebnem H.s.b.; Akgün, Haluk (2012-03-01)
Bu çalışma, Coğrafi Bilgi Sistemlerine (CBS) dayalı lojistik regresyon (LR) ve yapay sinir ağı (YSA) analizlerini kullanarak, Karadeniz bölgesindeki Bartın ilinin Ulus ilçesi için bir heyelan duyarlılık haritası hazırlamayı amaçlamaktadır. Bu araştırma kapsamında, Maden Tetkik ve Araştırma Genel Müdürlüğü tarafından hazırlanan heyelan envanter haritası, heyelan sınıflandırma haritasına temel olarak alınmıştır. Çalışma alanındaki analizlerin tamamı aktif heyelanlara istinaden gerçekleştirilmiştir. Bununla bi...
ABSOLUTE RADIOMETRIC CALIBRATION OF THE GOKTURK-2 SATELLITE SENSOR USING TUZ GOLU (LANDNET SITE) FROM NDVI PERSPECTIVE
Sakarya, Ufuk; Demirhan, Ismail Hakki; Deveci, Husne Seda; Teke, Mustafa; Demirkesen, Can; Kupcu, Ramazan; Oztoprak, A. Feray; Efendioglu, Mehmet; Simsek, F. Fehmi; Berke, Erdinc; Gurbuz, Sevgi Zubeyde (2016-07-19)
TOBITAK UZAY has conducted a research study on the use of space-based satellite resources for several aspects of agriculture. Especially, there are two precision agriculture related projects: HASSAS (Widespread application of sustainable precision agriculture practices in Southeastern Anatolia Project Region (GAP) Project) and AKTAR (Smart Agriculture Feasibility Project). The HASSAS project aims to study development of precision agriculture practice in GAP region. Multi-spectral satellite imagery and aeria...
Citation Formats
A. ÖZDARICI OK and S. Z. Akyürek, “Object Based Classification of Multi temporal Images for Agricultural Crop Mapping in Karacabey Plain Turkey,” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 127–132, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37791.