LearningBased Optimization of Hyperspectral Band Selection for Classification
Abstract
:1. Introduction
 We introduce a constrained measurement learning network that learns a binary mask for band selection.
 The measurement learning network and the classification network are jointly learned to minimize the classification loss, leading to optimally selected bands directly for the classification task.
 The number of selected bands is an additional constraint for the measurement learning network, and the proposed architecture can learn binary masks for any desired number of bands.
 The proposed architecture is flexible enough to adapt a new classification network that takes selected bands as its input, meaning that any new backpropagation adaptable classification network that performs better compared to our proposed classification model can replace the classification part of the proposed architecture, leading to further improvements in the performance.
Abbreviations
2. Background and Related Work
2.1. Unsupervised Hyperspectral Band Selection
2.2. Supervised Hyperspectral Band Selection
2.3. Deep Neural NetworkBased Measurement Learning
3. Proposed Method
3.1. LearningBased Optimization of Band Selection Pattern
3.2. Classification Network
Algorithm 1 MLBS Algorithm 

4. Datasets
5. Experimental Results
5.1. Experimental Setup
5.2. Joint Band Selection and Classification with MLBS
5.3. Quantitative Analysis and Comparisons
5.4. Computational Analysis
6. Discussion and Future Work
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Method  HBS Approach  Category  Brief Description of the Strategy 

MVPCA [11]  Unsupervised  Rankingbased  PCAbased ranking and high order selection 
FDPC [23]  Unsupervised  Clusteringbased  Distance clustering and density peak selection 
WaluDI [16]  Unsupervised  Clusteringbased  Information clustering and minmax optimization 
ISSC [21]  Unsupervised  Sparsitybased  Domain transform and orthogonal rank search with L2norm optimization 
SAEBS [25]  Unsupervised  Learningbased  Neural networkbased autoencoder training and selecting highest contributing bands 
MMCA [11]  Supervised  Rankingbased  Iterative band reduction with respect to misclassification error minimization 
MEAC [28]  Supervised  Searchbased  Iterative band selection with respect to covariance minimization 
CMCNN [26]  Supervised  Learningbased  Attention map generation with a neural network and selecting highest contributing bands 
BHCNN [27]  Supervised  Learningbased  Band selection with hard tresholding, learning theshold jointly with HSI classification 
Class Name  Method  

MEAC  BHCNN  CMCNN  MLBS  All Bands  
Alfalfa  76.47  69.05  65.22  70.39  36.59 
Notill corn  71.24  76.92  70.31  78.43  75.41 
Minimaltill corn  63.67  70.69  63.86  72.14  66.8 
Corn  64.04  71.23  58.02  65.16  59.14 
Grass/pasture  90.61  87.44  88.13  90.21  82.53 
Grass/trees  94.34  97.54  97.21  98.08  96.04 
Mowed grass/pasture  80.95  88.61  82.39  86  56 
Windrowed hay  97.49  98.65  96.54  99.05  98.61 
Oats  53.33  57.76  52.49  63.05  38.89 
Notill soybeans  70.78  79.37  76.56  81.98  66.4 
Minimaltill soybean  80.39  83.29  79.22  84.56  80.76 
Clean soybean  64.49  84.08  81.63  86.34  69.85 
Wheat  98.70  96.86  96.37  97.23  98.91 
Woods  93.36  97.47  96.89  98.15  94.38 
Buildings/grass/trees/drives  55.17  58.83  55.08  62.03  52.74 
Stone/steel towers  94.29  92.49  85.44  93.34  89.29 
ACA  78.08  81.89  77.84  82.88  72.65 
OCA  78.90  87.74  78.06  89.08  79.12 
KC  75.93  80.41  77.56  81.14  76.05 
Class Name  Method  

MEAC  BHCNN  CMCNN  MLBS  All Bands  
Asphalt  92.86  94.49  84.43  95.2  91.62 
Meadows  96.43  98.28  97.31  98.65  98.16 
Gravel  78.61  80.31  65.85  82.04  77.27 
Trees  93.55  94.83  86.02  95.42  89.75 
Painted Metal Sheets  99.59  99.38  93.43  99.03  98.95 
Bare Soil  84.11  89.57  79.03  91.76  90.14 
Bitumen  83.21  88.95  72.56  87.96  85.38 
Selfblocking Bricks  84.70  92.78  72.89  93.85  90.2 
Shadows  99.30  100  89.92  100  99.89 
ACA  90.26  93.17  82.38  93.77  91.26 
OCA  92.09  95.59  83.32  97.78  93.56 
KC  89.49  93.55  87.46  93.21  91.42 
Dataset  Method  

MEAC  CMCNN  BHCNN  MLBS  
IP  Training  1278  1275 s  1293 s  1216 s 
Inference  0.0904 s  0.1406  0.1093 s  0.0937 s  
UP  Training  4959 s  4877 s  5625 s  5050 s 
Inference  0.1099 s  0.1939 s  0.1406 s  0.1249 s 
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Ayna, C.O.; Mdrafi, R.; Du, Q.; Gurbuz, A.C. LearningBased Optimization of Hyperspectral Band Selection for Classification. Remote Sens. 2023, 15, 4460. https://doi.org/10.3390/rs15184460
Ayna CO, Mdrafi R, Du Q, Gurbuz AC. LearningBased Optimization of Hyperspectral Band Selection for Classification. Remote Sensing. 2023; 15(18):4460. https://doi.org/10.3390/rs15184460
Chicago/Turabian StyleAyna, Cemre Omer, Robiulhossain Mdrafi, Qian Du, and Ali Cafer Gurbuz. 2023. "LearningBased Optimization of Hyperspectral Band Selection for Classification" Remote Sensing 15, no. 18: 4460. https://doi.org/10.3390/rs15184460