IEEE Geoscience and Remote Sensing Magazine - March 2017 - 10

TABLE 1. A TERMINOLOGY OF CLASSIFICATION APPROACHES BASED ON DIFFERENT CRITERIA. TO NARROw DOwN THIS
ARTICLE'S RESEARCH LINE, wE INTENTIONALLY AVOID ELABORATING ON THE ITEMS HIGHLIGHTED IN GREEN.
CRITERIA

TYPES

BRIEF DESCRIPTION

Whether training samples are used
or not.

Supervised classifiers

Supervised approaches classify input data using a set of representative
samples for each class, known as training samples.

Unsupervised classifiers

Unsupervised approaches, also known as clustering, do not consider
the labels of training samples to classify the input data.

Semisupervised classifiers

The training step in semisupervised approaches is based on both
labeled and unlabeled training samples.

Parametric classifiers

Parametric classifiers are based on the assumption that the probability
density function for each class is known.

Nonparametric classifiers

Nonparametric classifiers are not constrained by any assumptions on
the distribution of input data.

Single-classifier classifiers

In this approach, a single classifier is taken into account to allocate a
class label for a given pixel.

Ensemble (multiple)
classifiers

In this approach, a set of classifiers (multiple classifiers) is taken into
account to allocate a class label for a given pixel.

Hard classifiers

Hard classification techniques do not consider the continuous changes
of different land-cover classes from one to another.

Soft (fuzzy) classifiers

Fuzzy classifiers model the gradual boundary changes by providing
measurements of the degree of similarity of all classes.

Spectral classifiers

This approach considers the hyperspectral image as a list of spectral
measurements with no spatial organization.

Spatial classifiers

This approach classifies the input data using spatially adjacent pixels,
based on either a crisp or adaptive neighborhood system.

Spectral-spatial classifiers

The sequence of spectral and spatial information is taken into account
for the classification of hyperspectral data.

Generative classifiers

This approach learns a model of the joint probability of the input and
the labeled pixels and makes the prediction using Bayes rules.

Discriminative classifiers

This approach learns conditional probability distribution or learns a
direct map from inputs to class labels.

Probabilistic classifiers

This approach is able to predict, given a sample input, a probability
distribution over a set of classes.

Nonprobabilistic classifiers

This approach simply assigns the sample to the most likely class that
the sample should belong to.

Subpixel classifiers

In this approach, the spectral value of each pixel is assumed to be a
linear or nonlinear combination of endmembers (pure
materials).

Per-pixel

Input pixel vectors are fed to classifiers as inputs.

Object- based and
object-oriented classifiers

In this approach, a segmentation technique allocates a label for each
pixel in the image in such a way that pixels with the same label share
certain visual characteristics. In this case, objects are known as
underlying units after applying segmentation. Classification is
conducted based on the objects instead of a single pixel.

Per-field classifiers

This type of classifier is obtained using a combination of RS and
geographic information system (GIS) techniques. In this context, raster
and vector data are integrated in a classification. The vector data are
often used to subdivide an image into parcels, and classification is
based on the parcels.

Whether any assumption on the
distribution of the input data is
considered or not.

Whether either a single classifier or
an ensemble classifier is taken into
account.

Whether or not the technique uses
hard partitioning, in which each data
point belongs to exactly one cluster.

Whether spatial information is taken
into account.

Whether the classifier learns a model
of the joint probability of the input
and the labeled pixels.

Whether the classifier predicts a
probability distribution over a set of
classes, given a sample input.

Which type of pixel information
is used.

supervised approaches, semisupervised techniques have
been introduced [10], [11]. With these, the training is based
on both labeled training samples as well as unlabeled samples. In the literature, it has been shown that the classification accuracy obtained with semisupervised approaches
can outperform that obtained by supervised classification.
In this article, our focus is only on supervised classification approaches.
10

SUPERVISED CLASSIFICATION OF
HYPERSPECTRAL DATA
A hyperspectral data set can be seen as a stack of many
pixel vectors, here denoted by x = (x 1, ..., x d) T , where d
represents the number of bands or the length of the pixel
vector. A common task when interpreting RS images is to
differentiate between several land-cover classes. A classification algorithm is used to separate between different types
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