This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP). First, class-dependent CR classifier (CDCRC) is used on HSI classification to fully make use of self-information contained in each class. Second, the MMP is included into the framework to discover local manifold structure. Combined with CDCRC, it formed the classifier named CDCRC based on MMP (CMCRC), which aims to reduce band redundancy. Finally, a comprehensive spectral-spatial classifier, called union of CMCRC, is proposed to optimize the classification map through integrating cumulative probability of residuals instead of applying strong constraints to maintain the spatial consistency. Experimental results on three real hyperspectral datasets demonstrate the effectiveness and practicality of the proposed methods over other related models for HSI classification tasks.

中文翻译：

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基于最大边距投影的高光谱影像分类的类相关协同表示联合

本文通过结合协作表示（CR）和最大边缘投影（MMP），提出了一种新的高光谱图像（HSI）光谱空间分类框架。首先，在 HSI 分类上使用类依赖 CR 分类器（CDCR），以充分利用每个类中包含的自我信息。其次，将 MMP 包含在框架中以发现局部流形结构。结合CDCR，形成了基于MMP的分类器CDCR(CMCRC)，旨在减少频带冗余。最后，提出了一种综合的光谱空间分类器，称为 CMCRC 的联合，通过整合残差的累积概率而不是应用强约束来优化分类图，以保持空间一致性。