Details
| Original language | English |
|---|---|
| Title of host publication | 2021 11th Workshop on Hyperspectral Imaging and Signal Processing |
| Subtitle of host publication | Evolution in Remote Sensing, WHISPERS 2021 |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (electronic) | 978-1-6654-3601-4 |
| Publication status | Published - 2021 |
| Event | 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - online Duration: 24 Mar 2021 → 26 Mar 2021 |
Publication series
| Name | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing |
|---|---|
| Volume | 2021-March |
| ISSN (Print) | 2158-6276 |
Abstract
For transmitting the large amount of hyperspectral image (HSI) data over a small data link from a small platform to the ground, an efficient data compression with low computational cost has to be done at the platform. Additionally, spectral band reduction interpreted as preprocessing of the compression is reasonable. We present a method for hyperspectral band reduction using a modified convolutional neural network (CNN) which retains the information about the spectral origin from layer to layer until it can be assigned directly to the classes to be classified. The relevant bands for each class are determined. Experimental verification shows that the network architecture using only the relevant bands has improved stability and results in a better overall performance.
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2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021. 2021. p. 1-5 9483986 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2021-March).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Determination of Relevant Hyperspectral Bands Using a Spectrally constrained CNN
AU - Pestel-Schiller, Ulrike
AU - Hu, Kai
AU - Gritzner, Daniel
AU - Ostermann, Jörn
PY - 2021
Y1 - 2021
N2 - For transmitting the large amount of hyperspectral image (HSI) data over a small data link from a small platform to the ground, an efficient data compression with low computational cost has to be done at the platform. Additionally, spectral band reduction interpreted as preprocessing of the compression is reasonable. We present a method for hyperspectral band reduction using a modified convolutional neural network (CNN) which retains the information about the spectral origin from layer to layer until it can be assigned directly to the classes to be classified. The relevant bands for each class are determined. Experimental verification shows that the network architecture using only the relevant bands has improved stability and results in a better overall performance.
AB - For transmitting the large amount of hyperspectral image (HSI) data over a small data link from a small platform to the ground, an efficient data compression with low computational cost has to be done at the platform. Additionally, spectral band reduction interpreted as preprocessing of the compression is reasonable. We present a method for hyperspectral band reduction using a modified convolutional neural network (CNN) which retains the information about the spectral origin from layer to layer until it can be assigned directly to the classes to be classified. The relevant bands for each class are determined. Experimental verification shows that the network architecture using only the relevant bands has improved stability and results in a better overall performance.
UR - http://www.scopus.com/inward/record.url?scp=85112815614&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS52202.2021.9483986
DO - 10.1109/WHISPERS52202.2021.9483986
M3 - Conference contribution
SN - 978-1-6654-1174-5
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
SP - 1
EP - 5
BT - 2021 11th Workshop on Hyperspectral Imaging and Signal Processing
T2 - 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Y2 - 24 March 2021 through 26 March 2021
ER -