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Training sample selection is a great challenge for hyperspectral image classification (HSIC), specifically when only a very limited number of labeled data samples are available for training. Two recently developed concepts for training sample selection are of particular interest. One is active learning (AL), which augments labeled training samples by including unlabeled data samples as new training samples. The other is iterative training sampling (ITS), which expands data cubes by including additional spatial classification information into the training samples. This article combines AL and ITS simultaneously to derive a joint ITS–AL spectral–spatial (SS) classification approach, to be called ITS augmentation by AL spectral–spatial classification, referred to as ITSA-AL-SS, which can improve SS classification using either AL or ITS alone. The novel idea of ITSA-AL-SS is to take advantage of ITS to expand data cubes by incorporating additional spatial classification information iteratively into the AL-augmented unlabeled data samples to update the current training samples iteration by iteration in one-shot operation. As expected, ITSA-AL-SS is benefited from both ITS and AL to not only further improve classification accuracy but also reduce classification inconsistency.

Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set of training samples may produce a different classification result. A general approach to addressing this problem is the so-called K -fold method which implements RTS K times and takes the average of overall accuracy with respect to standard deviation to describe a confidence level of classification performance. To deal with this issue, this article develops an iterative RTS (IRTS) method as an alternative to the K -fold method to reduce the uncertainty caused by RTS. Its idea is to add the spatial filtered classification maps to the image cube that is currently being processed via feedback loops to augment image cubes iteratively. Then, the training samples will be reselected randomly from the new augmented image cubes iteration-by-iteration. As a result, the training samples selected from each iteration will be updated by new added spatial information captured by spatial filters implemented at the iteration. The experimental results clearly demonstrate that IRTS successfully improves classification accuracy as well as reduces inconsistency in results.

Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in hyperspectral image classifications is the random training sample selection which produces inconsistent results. A general approach to resolving this problem is so-called k-fold method which implements randomly selected training samples k times and takes their average with respect to the standard deviation to be used describe a confidence interval. This paper develops an approach to mitigating such a random issue by introducing an iterative process to remove uncertainty caused by randomness. Its idea is to repeatedly feedback the classification results in an iterative manner that the randomness caused by the randomly selected samples can be largely reduced. The iterative process is terminated as long as the classification results obtained by two consecutive iterations agree with a prescribed tolerance. Experimental results demonstrate that our proposed method works very effectively not only to reduce result inconsistency but also to improve classification results.

This paper presents an emerging method to detect pesticide residues on fruit. In order to enhance pesticide signature intensity and make the detection rate of pesticide better, we applied band weighting process and band selection (BS) process base on band prioritization (BP) and band decorrelation (BD) to adjust spectral data. Then four algorithms were used, spectral information divergence (SID), orthogonal subspace projection (OSP), constrained energy minimization (CEM), and support vector machine (SVM) to identify pesticide residues on different fruit. The results show that using CEM method has the highest detection rate of pesticide and has the potential to replace the other traditional methods.