SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELDS CROP CLASSIFICATION FROM TERRASAR-X IMAGES
نویسندگان
چکیده
منابع مشابه
Spatial-temporal Conditional Random Fields Crop Classification from Terrasar-x Images
The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while global demand for food has grown twofold. There is need to monitor and update agriculture land-cover to support food security measures. This study develops a spatial-temporal approach using conditional random fields (CRF) to classify co-registered im...
متن کاملMulti-temporal Classification for Crop Discrimination using TerraSAR – X Spotlight images
Within the past years investigations have been carried out on the usefulness of ENVISAT ASAR dual polarimetric data for environmental mapping of agricultural areas. The performance of such approach is limited by the spatial resolution of the data, which is firstly too coarse for sufficient sampling of small fields often found in Europe and secondly useful features related to agricultural treatm...
متن کاملEfficient Spatial Classification Using Decoupled Conditional Random Fields
We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accurac...
متن کاملConditional Random Fields for Airborne Lidar Point Cloud Classification in Urban Area
Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in imaging and non-imaging sensor technologies, s...
متن کاملHidden conditional random fields for phone classification
In this paper, we show the novel application of hidden conditional random fields (HCRFs) – conditional random fields with hidden state sequences – for modeling speech. Hidden state sequences are critical for modeling the non-stationarity of speech signals. We show that HCRFs can easily be trained using the simple direct optimization technique of stochastic gradient descent. We present the resul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2015
ISSN: 2194-9050
DOI: 10.5194/isprsannals-ii-3-w4-79-2015