Hyperspectral imaging (HSI) greatly improves the capacity to identify and monitor ground objects due to the high spectral resolution. As the real-time remote sensing monitoring and warning tasks are getting more attention, new algorithms for low-power on-board classification are required to reduce the transmission time of satellite downlink. In this article, we propose the multiscale local maximum random forest (MSLM-RF) to significantly reduce energy consumption while retaining high classification accuracy. The proposed MSLM-RF uses multiscale maximum filters for spatial feature extraction and random forest for classification after spectral and spatial features fusion. The spatial features are efficiently extracted with low computational complexity by regarding the maximum light intensity values in different ranges of pixels as anchor points. MSLM-RF only consists of integer comparisons and a few additions, thereby eliminating the energy-hungry operations such as multiplication and exponentiation. According to experimental results on the HSI benchmark datasets, MSLM-RF delivers a better tradeoff in accuracy and computational complexity than the state-of-the-art classification algorithms. Besides, MSLM-RF gets higher average classification accuracy and lower energy consumption than the previous on-board algorithms. The obtained results show the suitability of the proposed algorithm to accomplish practical real-time classification tasks on-board with low energy consumption.

中文翻译：

---

MSLM-RF：用于车载高光谱图像分类的空间特征增强随机森林

由于高光谱分辨率，高光谱成像 (HSI) 极大地提高了识别和监测地面物体的能力。随着实时遥感监测预警任务越来越受到重视，需要新的低功耗星载分类算法来减少卫星下行链路的传输时间。在本文中，我们提出了多尺度局部最大随机森林 (MSLM-RF)，以显着降低能耗，同时保持较高的分类精度。所提出的 MSLM-RF 使用多尺度最大滤波器进行空间特征提取，并在光谱和空间特征融合后使用随机森林进行分类。通过将不同像素范围内的最大光强值作为锚点，以较低的计算复杂度有效地提取空间特征。MSLM-RF 仅包含整数比较和一些加法，从而消除了诸如乘法和取幂之类的耗能运算。根据 HSI 基准数据集的实验结果，MSLM-RF 在准确性和计算复杂性方面比最先进的分类算法提供了更好的折衷。此外，MSLM-RF 比之前的板载算法获得了更高的平均分类精度和更低的能耗。所获得的结果表明，所提出的算法适用于以低能耗完成实际的车载实时分类任务。与最先进的分类算法相比，MSLM-RF 在准确性和计算复杂性方面提供了更好的权衡。此外，MSLM-RF 比之前的板载算法获得了更高的平均分类精度和更低的能耗。所获得的结果表明，所提出的算法适用于以低能耗完成实际的车载实时分类任务。与最先进的分类算法相比，MSLM-RF 在准确性和计算复杂性方面提供了更好的权衡。此外，MSLM-RF 比之前的板载算法获得了更高的平均分类精度和更低的能耗。所获得的结果表明，所提出的算法适用于以低能耗完成实际的车载实时分类任务。