KEYWORDS: Telecommunications, Data processing, Image processing, Human-machine interfaces, Algorithm development, Hyperspectral imaging, Data acquisition, Data conversion, Data communications, Imaging systems
With the continuous expansion of hyperspectral application scenarios, the traditional universal hyperspectral data processing software system is difficult to meet the needs of the industry, and cannot be quickly connected to the intelligent processing algorithm developed by the industry, which has become one of the bottlenecks in the promotion of hyperspectral to practical applications. In order to meet the needs of various industries for professional processing of hyperspectral data, fast access to intelligent processing algorithm, and highly efficient and reliable transplantation of intelligent processing algorithm to airborne platform, this paper designs an airborne hyperspectral data processing platform compatible with intelligent processing algorithms. The software architecture of "Platform + Plug-in" is realized, which provides comprehensive support for hyperspectral image processing and enables users to focus on the development of intelligent processing algorithms, which can be compatible with different intelligent processing algorithms through simple configuration.
Traditional hyperspectral feature extraction methods focus on spectral features and neglect spatial features,its extraction method is set in advance and is not suitable for all hyperspectral images. Faced with these problems, we propose a three-dimensional convolutional network for hyperspectral classification, which consists of a convolutional layer,2 downsampling layers, 2 identification layers, a flatten layer, and 4 fully connected layers. The proposed network employs three-dimensional convolution operation to extract spectral-spatial features from hyperspectral images,there are two reasons for this, the first reason is three-dimensional convolution can automatically learn a large number of mappings between input and output.The second reason is three-dimensional convolution can effectively extract spectral-spatial features and improve network classification performance. In order to extract high-level features and prevent network performance degradation, the proposed network adopts residual connections.More importantly, the OpenMax algorithm is employed to detect hyperspectral unknown targets. In addition to the probability that the output belongs to a known class, the OpenmMx adds the probability that the predicted input belongs to unknown classes, as a result,the deep convolutional network can respond to inputs of unknown classes.experiments based on typical hyperspectral data show that the proposed network perform accurately in the known classes classification and the openmax algorithm is suitable for unknown targets detection of hyperspectral images.
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