Identification of hazelnut fields using spectral and Gabor textural features

By S. Reis, K. Tasdemir. 2011

63 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/71cdc9e1b4315c9073b477d09930c8bdfe258bd8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Huapeng Li
2. Xin Pan
3. Ce Zhang
4. Peter M. Atkinson
5. Isabel Sargent
6. Jonathon S. Hare
7. A. Gardiner

In Ecology

1. Huapeng Li
2. Xin Pan
3. Ce Zhang
4. Peter M. Atkinson
5. Isabel Sargent
6. Jonathon S. Hare
7. A. Gardiner

In Media Technology

1. Huapeng Li
2. Xin Pan
3. Ce Zhang
4. Peter M. Atkinson
5. Isabel Sargent
6. Jonathon S. Hare
7. A. Gardiner

Citations in the corpus

• An object-based convolutional neural network (OCNN) for urban land use classification (2018, methodology): “…often weakened by the pixel-wise 676 convolutional process with image patches, where blurred boundaries occur between the 677 classified objects with a loss of small useful land features, somewhat similar to morphological 678 or Gabor filter methods (Pingel et al., 2013; Reis and Tasdemir, 2011).”
• A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification (2017, background): “Boundary uncertainties (over-smoothness) often appear 569 in the classified object and small useful features are erased, somewhat similar to 570 morphological or Gabor filter methods (Pingel et al., 2013; Reis and Tasdemir, 2011).”