Abstract
Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification.
Highlights
In order to achieve the classification of hyperspectral image (HSI) of n bands, we reduce the spectral dimension from n to r n; firstly, there are various dimension reduction techniques can be used, in this paper, principal component analysis (PCA) is chosen due to its widespread use with
The results in the table illustrate that the method proposed in this paper achieves the best results on the three evaluation criteria of OA, AA and Kappa coefficients, that is to say, the model proposed in this paper can effectively improve the classification accuracy of hyperspectral images, which further confirms the effectiveness and superiority of the proposed method
Hyperspectral image classification is of significant value in remote sensing analysis, including the latest trend of satellite Internet of Things (IoT), which can be applied in various scenarios, such as crop supervision, forest management, urban development and risk management
Summary
The emergence of Satellite Internet of Things(IoT) system, which means combining various information sensor equipments with network into a huge network through satellite communication, has a profound impact on processing. With the emergence of new acquisition platforms, smaller and more efficient sensors, and edge computing [1], remote sensing technology is once again on the edge of major technological innovation. Remote sensing was a subject of aerial surveying and mapping, geographic information systems, and earth observation, but recent developments have shifted it to the direction of satellite Internet of Things. The continuous streaming data from the interconnected devices on the aggregation platform will paint a vivid picture
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