Giordano Rossella, Guccione Pietro
Department of Electrical and Information Engineering, Politecnico di Bari, 70125 Bari, Italy.
Sensors (Basel). 2017 May 19;17(5):1160. doi: 10.3390/s17051160.
In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA's CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target's related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.
近年来，用于地球遥感的高光谱传感器变得非常流行。这类系统能够为用户提供具有光谱和空间信息的图像。当前的高光谱星载传感器能够以更高的空间和光谱分辨率捕获大面积区域。因此，为了避免由于存储空间有限导致轨道占空比降低，需要在机载时减少采集数据的量。最近，文献将注意力集中在机载数据压缩的有效方法上。由于恶劣的环境（外层空间）以及有限的时间、功率和计算资源，这个课题是一项具有挑战性的任务。通常，图形处理单元（GPU）的硬件特性已被用于通过并行计算来减少处理时间。当前的工作提出了一个在GPU上进行机载操作的框架，使用英伟达的CUDA（计算统一设备架构）架构。该算法旨在使用与目标相关的策略进行机载压缩。详细来说，主要操作包括：使用无监督分类器在感兴趣区域（这是用户相关的选择）中近实时自动识别土地覆盖类型或检测事件；使用包括主成分分析（PCA）、小波和算术编码在内的具有空间可变不同比特率对特定区域进行压缩；以及对地面站的数据量管理。使用从港口区域的机载可见/红外成像光谱仪（AVIRIS）机载传感器获取的真实数据集进行了实验。
