Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.3390/rs1040875 |
| Title (Primary) | Supervised classification of agricultural land cover using a modified k-NN technique (MNN) and landsat remote sensing imagery |
| Author | Samaniego, L.; Schulz, K. |
| Journal | Remote Sensing |
| Year | 2009 |
| Department | CHS |
| Volume | 1 |
| Issue | 4 |
| Page From | 875 |
| Page To | 895 |
| Language | englisch |
| Keywords | Land use classification; supervised classification; nearest neighbors; agricultural land cover; crops |
| Abstract | Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=554 |
| Samaniego, L., Schulz, K. (2009): Supervised classification of agricultural land cover using a modified k-NN technique (MNN) and landsat remote sensing imagery Remote Sens. 1 (4), 875 - 895 |
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