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Copyright

All Rights Reserved

Permission to use, copy, modify, and distribute this software and its documentation for any non-commercial purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

BAMS-FE

The feature extraction code of "BAMS-FE: Band-by-Band Adaptive Multiscale Superpixel Feature Extraction for Hyperspectral Image Classification". Here we provide two versions of the BAMS algorithm: one in Python and the other in MATLAB. The Python version requires calling MATLAB functions. The algorithm used in our paper is based on the Python version. The file ERS.m needs to call the entropy superpixels segmentation algorithm, which is available as an open-source algorithm on GitHub (https://github.com/mingyuliutw/EntropyRateSuperpixel). Please make sure to prepare this algorithm before running the code.

2024.02.21更新

本次更新提交了一个demo,提交了由ERS编译而来的mex文件,本demo可以直接运行(前提是配置好python和matlab的环境)