Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data

The problem of classification is shared across various disciplines.
Designing even less computationally demanding and more effective
classifiers has been a key challenge for researchers for many years. No
single classifier can be highly effective for all types of datasets and
thus, depending on the data distribution, various classifiers have
been proposed in the literature. To our knowledge, feature values
have been vastly exploited as the base for discriminating classes,
while feature sequence information has been somehow underexploited so far. In the proposed approach normalised features are
sorted and ranked, creating a sequence of finite numbers. The associated rank of the created sequence is used as an additional feature,
which in a way defines the sample-specific intra-feature relationship.
Three novel dictionary-based approaches such as Sequence Classifier
(SC), Sequence-dictionary-based k-Nearest Neighbours Classifier
(SDk-NN) and Combined-dictionary-based k-Nearest Neighbours
Classifier (CDk-NN) are proposed in this paper.
In the case of remotely sensed data, and specifically in HyperSpectral Images (HSI), the spectral features (Spectral signatures)
represent a strong, object-specific spectral relationship, which is
a key point in our proposed approach. In this case, indeed, the
proposed classifiers were tested over various (five) HS datasets and
found to be effective. Based on the classifiers features, two derived
distance measures are proposed and validated for the HS dataset,
namely: the Normalised Sequence Distance (NSD) measure and
Combined Distance (CD) measure. These measures appear to overperform the conventional Normalised Euclidean Distance (NED) in
this context. Also, validation for both binary and multi-class datasets
are experimented and their performances are evaluated in terms of
accuracy and other standard measures. Experimental results over 21
datasets revealed that the proposed approaches perform comparably, and in some cases even better than other classifiers. Stackoperated, class-specific sparse dictionaries are also introduced in
order to reduce the computational complexity, which can be used
as an active learning-based approach for optimal training sample
selection. Additional tests were performed with variable levels of
dictionary sparsity for assessing its impact on accuracy