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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Supervised Hyperspectral Image Classification With Rejection
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Supervised Hyperspectral Image Classification With Rejection

机译：带排斥的监督高光谱图像分类

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摘要

Hyperspectral image classification is a challenging problem as obtaining complete and representative training sets is costly, pixels can belong to unknown classes, and it is generally an ill-posed problem. The need to achieve high classification accuracy may surpass the need to classify the entire image. To account for this scenario, we use classification with rejection by providing the classifier with an option not to classify a pixel and consequently reject it. We present and analyze two approaches for supervised hyperspectral image classification that combine the use of contextual priors with classification with rejection: 1) by jointly computing context and rejection and 2) by sequentially computing context and rejection. In the joint approach, rejection is introduced as an extra class that models the probability of classifier failure. In the sequential approach, rejection results from the hidden field associated with a marginal maximum a posteriori classification of the image. We validate both approaches on real hyperspectral data.
机译：高光谱图像分类是一个具有挑战性的问题，因为获得完整且具有代表性的训练集非常昂贵，像素可能属于未知类别，并且通常是不适定的问题。实现高分类精度的需求可能会超过对整个图像进行分类的需求。为了解决这种情况，我们通过为分类器提供不对像素进行分类并因此拒绝像素的选项来使用拒绝分类。我们提出并分析了两种监督超光谱图像分类的方法，这些方法将上下文先验与分类与拒绝结合起来使用：1）通过联合计算上下文和拒绝，以及2）通过顺序计算上下文和拒绝。在联合方法中，拒绝被引入作为对分类器失败概率进行建模的额外类。在顺序方法中，拒绝是由与图像的边缘最大后验分类相关联的隐藏字段导致的。我们在真实的高光谱数据上验证这两种方法。

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