TY - JOUR
T1 - Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries
AU - Yang, Shujun
AU - Hou, Junhui
AU - Jia, Yuheng
AU - Mei, Shaohui
AU - Du, Qian
PY - 2020/9
Y1 - 2020/9
N2 - In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI)
classification, namely SR with incremental dictionaries (SRID).
Our SRID boosts existing SR-based HSI classification methods
significantly, especially when used for the task with extremely
limited training samples. Specifically, by exploiting unlabeled
pixels with spatial information and multiple-feature-based SR
classifiers, we select and add some of them to dictionaries in
an iterative manner, such that the representation abilities of
the dictionaries are progressively augmented, and likewise more
discriminative representations. In addition, to deal with largescale data sets, we use a certainty sampling strategy to control the
sizes of the dictionaries, such that the computational complexity
is well balanced. Experiments over two benchmark data sets show
that our proposed method achieves higher classification accuracy
than the state-of-the-art methods, i.e., the overall classification
accuracy can improve more than 4%.
AB - In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI)
classification, namely SR with incremental dictionaries (SRID).
Our SRID boosts existing SR-based HSI classification methods
significantly, especially when used for the task with extremely
limited training samples. Specifically, by exploiting unlabeled
pixels with spatial information and multiple-feature-based SR
classifiers, we select and add some of them to dictionaries in
an iterative manner, such that the representation abilities of
the dictionaries are progressively augmented, and likewise more
discriminative representations. In addition, to deal with largescale data sets, we use a certainty sampling strategy to control the
sizes of the dictionaries, such that the computational complexity
is well balanced. Experiments over two benchmark data sets show
that our proposed method achieves higher classification accuracy
than the state-of-the-art methods, i.e., the overall classification
accuracy can improve more than 4%.
KW - Hyperspectral image (HSI) classification
KW - incremental learning
KW - multiple features
KW - sparse representation (SR)
KW - Hyperspectral image (HSI) classification
KW - incremental learning
KW - multiple features
KW - sparse representation (SR)
KW - Hyperspectral image (HSI) classification
KW - incremental learning
KW - multiple features
KW - sparse representation (SR)
UR - http://www.scopus.com/inward/record.url?scp=85087634899&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85087634899&origin=recordpage
U2 - 10.1109/LGRS.2019.2949721
DO - 10.1109/LGRS.2019.2949721
M3 - RGC 21 - Publication in refereed journal
SN - 1545-598X
VL - 17
SP - 1598
EP - 1602
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 9
M1 - 8892586
ER -