ABSTRACT Deep networks have escalated the computational performance in the sensor-based high dimensional imaging such as hyperspectral images (HSI), due to their informative feature extraction competency. Therefore in this work, we have extracted the informative features from different CNN models for the benchmark HSI datasets. The deep features have concatenated with spectral features to increase the informative knowledge in the image datacube. The feature concatenation has massively increased the size of datacube. Therefore, we have applied an unsupervised maximum object identification-based salient feature selection to identify the most informative features of datacube and discard the less informative features to reduce the computational time without compromising the accuracy. It is an unsupervised feature selection approach that transforms the data into scale space and achieved robust and strong features. In the previous CNN-based methods, raw features have directly fed to the MLP (multilayer perception) layers for target prediction whereas we have provided our salient features into a multi-core SVM-based set-up and have achieved high accuracy with low computational time as compared to the previous state-of-art techniques.

中文翻译：

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HSI图像语义目标预测中基于CNN的显着特征

摘要 由于深度网络的信息特征提取能力，深度网络已经提升了基于传感器的高维成像（如高光谱图像（HSI））的计算性能。因此，在这项工作中，我们从基准 HSI 数据集的不同 CNN 模型中提取了信息特征。深层特征与光谱特征相结合，以增加图像数据立方体中的信息知识。特征串联极大地增加了数据立方体的大小。因此，我们应用了一种无监督的基于最大对象识别的显着特征选择来识别数据立方体中信息量最大的特征，并丢弃信息量较少的特征，以在不影响准确性的情况下减少计算时间。它是一种无监督的特征选择方法，将数据转化为尺度空间，实现了鲁棒性强的特征。在之前基于 CNN 的方法中，原始特征直接馈送到 MLP（多层感知）层进行目标预测，而我们将显着特征提供到基于多核 SVM 的设置中，并以低计算量实现了高精度与以前最先进的技术相比，时间。