ee.Algorithms.Image.Segmentation.SNIC

Pengelompokan superpiksel berdasarkan SNIC (Simple Non-Iterative Clustering). Menghasilkan rentang ID cluster dan rata-rata per cluster untuk setiap rentang input. Jika gambar 'seeds' tidak diberikan sebagai input, output akan menyertakan band 'seeds' yang berisi lokasi bibit yang dihasilkan.

Lihat: Achanta, Radhakrishna dan Susstrunk, Sabine, 'Superpixels and Polygons using Simple Non-Iterative Clustering', CVPR, 2017.

Penggunaan	Hasil
ee.Algorithms.Image.Segmentation.SNIC(image, size, compactness, connectivity, neighborhoodSize, seeds)	Gambar

Argumen	Jenis	Detail
image	Gambar	Gambar input untuk pengelompokan.
size	Bilangan bulat, default: 5	Jarak lokasi titik awal superpiksel, dalam piksel. Jika gambar 'seed' disediakan, tidak ada petak yang dihasilkan.
compactness	Float, default: 1	Faktor kepadatan. Nilai yang lebih besar menyebabkan cluster menjadi lebih ringkas (persegi). Menyetelnya ke 0 akan menonaktifkan pembobotan jarak spasial.
connectivity	Bilangan bulat, default: 8	Konektivitas. 4 atau 8.
neighborhoodSize	Bilangan bulat, default: null	Ukuran lingkungan petak (untuk menghindari artefak batas petak). Defaultnya adalah 2 * ukuran.
seeds	Gambar, default: null	Jika disediakan, piksel bernilai non-nol akan digunakan sebagai lokasi awal. Piksel yang bersentuhan (seperti yang ditentukan oleh 'konektivitas') dianggap termasuk dalam cluster yang sama.

Contoh

Editor Kode (JavaScript)

// Note that the compactness and size parameters can have a significant impact
// on the result. They must be adjusted to meet image-specific characteristics
// and patterns, typically through trial. Pixel scale (map zoom level) is also
// important to consider. When exploring interactively through map tile
// visualization, the segmentation result it dependent on zoom level. If you
// need to evaluate the result at a specific scale, call .reproject() on the
// result, but do so with caution because it overrides the default scaling
// behavior that makes tile computation fast and efficient.


// Load a NAIP image for a neighborhood in Las Vegas.
var naip = ee.Image('USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613');

// Apply the SNIC algorithm to the image.
var snic = ee.Algorithms.Image.Segmentation.SNIC({
  image: naip,
  size: 30,
  compactness: 0.1,
  connectivity: 8,
});

// Display the original NAIP image as RGB.
// Lock map zoom to maintain the desired scale of the segmentation computation.
Map.setLocked(false, 18, 18);
Map.setCenter(-115.32053, 36.182016, 18);
Map.addLayer(naip, null, 'NAIP RGB');

// Display the clusters.
Map.addLayer(snic.randomVisualizer(), null, 'Clusters');

// Display the RGB cluster means.
var visParams = {
  bands: ['R_mean', 'G_mean', 'B_mean'],
  min: 0,
  max: 255
};
Map.addLayer(snic, visParams, 'RGB cluster means');

Penyiapan Python

Baca halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

# Note that the compactness and size parameters can have a significant impact
# on the result. They must be adjusted to meet image-specific characteristics
# and patterns, typically through trial. Pixel scale (map zoom level) is also
# important to consider. When exploring interactively through map tile
# visualization, the segmentation result it dependent on zoom level. If you
# need to evaluate the result at a specific scale, call .reproject() on the
# result, but do so with caution because it overrides the default scaling
# behavior that makes tile computation fast and efficient.


# Load a NAIP image for a neighborhood in Las Vegas.
naip = ee.Image('USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613')

# Apply the SNIC algorithm to the image.
snic = ee.Algorithms.Image.Segmentation.SNIC(
    image=naip, size=30, compactness=0.1, connectivity=8
)

# Display the original NAIP image as RGB.
m = geemap.Map()
m.set_center(-115.32053, 36.182016, 18)
m.add_layer(naip, None, 'NAIP RGB')

# Display the clusters.
m.add_layer(snic.randomVisualizer(), None, 'Clusters')

# Display the RGB cluster means.
vis_params = {'bands': ['R_mean', 'G_mean', 'B_mean'], 'min': 0, 'max': 255}
m.add_layer(snic, vis_params, 'RGB cluster means')
m