Abstract--- Deep neural network has been extensively applied to hyperspectral image (HSI) classification recently. However, its success is greatly attributed to numerous labeled samples, whose acquisition costs a large amount of time and money. In order to improve the classification performance while reducing the labeling cost, this article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework. First, we train a convolutional neural network (CNN) with a limited number of labeled pixels. Next, we actively select the most informative pixels from the candidate pool for labeling. Then, the CNN is fine-tuned with the new training set constructed by incorporating the newly labeled pixels. This step together with the previous step is iteratively conducted. Finally, Markov random field (MRF) is utilized to enforce class label smoothness to further boost the classification performance. Compared with the other state-of-the-art traditional and deep learning-based HSI classification methods, our proposed approach achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples. Index Terms--- Active learning (AL), convolutional neural network (CNN), deep learning, hyperspectral image (HSI) classification, Markov random field (MRF).
data.IndBand = {[10, 80, 200], [12, 67, 98], [10, 60, 90]}; % to generate false RGB, which should be less contaminated bands
%data.flagPCA = true;
%data.ReducedDim = 10;
% Three datasets:
% data.flagSet = 1, Indian Pines;
% = 2, Pavia University;
% = 3, Pavia Center.
data.flagSet = 1;
data.NameFolder = data.NameFolder{data.flagSet};
data.SizeOri = data.SizeOri{data.flagSet};
data.NumClass = data.NumClass{data.flagSet};
data.IndBand = data.IndBand{data.flagSet};
%% Parameters for algorithm
alg.SampleSty = 'Rd'; % out of {'Rd', 'Classwise'}
alg.CountSty = 'Num'; % out of {'Num', 'Ratio'}
alg.NumTrn1st = {250, 107, 58};
alg.NumTrn1st = alg.NumTrn1st{data.flagSet};
% if alg.CountSty == 'Ratio'
%alg.RatioTrn1st = {0.02, 0.0025, 0.0025};
%alg.RatioTrn1st = alg.RatioTrn1st{data.flagSet};
alg.CrossVal = 0.05;
alg.NumAlAugPerIte = {[250, 150, 100, 50], [107, 107, 107], [26, 20]}; % The training samples added in each iteration keeps the same ratio with the training sample number of the first iteration
alg.AlStra = 'BvSB'; % out of {'BvSB', 'RS', 'EP'};
🎉3参考文献
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[1] Xiangyong Cao, Jing Yao, Zongben Xu, Deyu Meng. Hyperspectral Image Classification with Convolutional Neural Network and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, 2020.
[2] H. Bi, F. Xu, Z. Wei, Y. Xue, and Z. Xu, An active deep learning approach for minimally supervised polsar image classification. IEEE Transactions on Geoscience and Remote Sensing, 2019.
