Data
satellite_image

satellite_image

active ARFF Publicly available Visibility: public Uploaded 17-08-2014 by Tobias Kuehn
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Author: Source: Unknown - 1993 Please cite: Source: Ashwin Srinivasan Department of Statistics and Data Modeling University of Strathclyde Glasgow Scotland UK ross '@' uk.ac.turing The original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for Remote Sensing, and used for research at: The Centre for Remote Sensing University of New South Wales Kensington, PO Box 1 NSW 2033 Australia. The sample database was generated taking a small section (82 rows and 100 columns) from the original data. The binary values were converted to their present ASCII form by Ashwin Srinivasan. The classification for each pixel was performed on the basis of an actual site visit by Ms. Karen Hall, when working for Professor John A. Richards, at the Centre for Remote Sensing at the University of New South Wales, Australia. Conversion to 3x3 neighbourhoods and splitting into test and training sets was done by Alistair Sutherland. Data Set Information: The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. The Landsat satellite data is one of the many sources of information available for a scene. The interpretation of a scene by integrating spatial data of diverse types and resolutions including multispectral and radar data, maps indicating topography, land use etc. is expected to assume significant importance with the onset of an era characterised by integrative approaches to remote sensing (for example, NASA's Earth Observing System commencing this decade). Existing statistical methods are ill-equipped for handling such diverse data types. Note that this is not true for Landsat MSS data considered in isolation (as in this sample database). This data satisfies the important requirements of being numerical and at a single resolution, and standard maximum-likelihood classification performs very well. Consequently, for this data, it should be interesting to compare the performance of other methods against the statistical approach. One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels. The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel. The number is a code for the following classes: Number Class 1 red soil 2 cotton crop 3 grey soil 4 damp grey soil 5 soil with vegetation stubble 6 mixture class (all types present) 7 very damp grey soil NB. There are no examples with class 6 in this dataset. The data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset. In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary. Attribute Information: The attributes are numerical, in the range 0 to 255. UCI: http://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)

37 features

class (target)numeric6 unique values
0 missing
attr1numeric51 unique values
0 missing
attr2numeric84 unique values
0 missing
attr3numeric76 unique values
0 missing
attr4numeric102 unique values
0 missing
attr5numeric51 unique values
0 missing
attr6numeric82 unique values
0 missing
attr7numeric76 unique values
0 missing
attr8numeric103 unique values
0 missing
attr9numeric50 unique values
0 missing
attr10numeric81 unique values
0 missing
attr11numeric78 unique values
0 missing
attr12numeric104 unique values
0 missing
attr13numeric51 unique values
0 missing
attr14numeric83 unique values
0 missing
attr15numeric78 unique values
0 missing
attr16numeric101 unique values
0 missing
attr17numeric50 unique values
0 missing
attr18numeric80 unique values
0 missing
attr19numeric77 unique values
0 missing
attr20numeric104 unique values
0 missing
attr21numeric50 unique values
0 missing
attr22numeric80 unique values
0 missing
attr23numeric78 unique values
0 missing
attr24numeric104 unique values
0 missing
attr25numeric51 unique values
0 missing
attr26numeric82 unique values
0 missing
attr27numeric75 unique values
0 missing
attr28numeric102 unique values
0 missing
attr29numeric50 unique values
0 missing
attr30numeric81 unique values
0 missing
attr31numeric77 unique values
0 missing
attr32numeric103 unique values
0 missing
attr33numeric50 unique values
0 missing
attr34numeric80 unique values
0 missing
attr35numeric77 unique values
0 missing
attr36numeric104 unique values
0 missing

19 properties

6435
Number of instances (rows) of the dataset.
37
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
37
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.59
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

18 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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