Spectral variability and shadow effects can limit the hyperspectral image (HSI) classification performance. Compared with HSI, the LiDAR data is an excellent complement with its abundant elevation information. In this study, a procedure including pre-processing, deep residual network classification and post-processing is investigated for classification of HSI aided by the LiDAR data to release the problem of identifying shaded objects and spectral variability. Specifically, three aspects with respect to spectral band selection using Archetypal Analysis (AA), feature level fusion based classification by deploying a residual network associated approach and label correction utilizing the elevation information, are explored to realize more accurate classification. Experiments on three public multi-source (hyperspectral and LiDAR) remote sensing datasets show more promising classification can be achieved via fusion two-source of remote sensing data than that using only independent hyperspectral image. In particular, on the Houston 2017 dataset, OA and Kappa achieved significant gains of 2.47% and 2.79% respectively after incorporating LiDAR information. Moreover, the results demonstrate the elevation information used independently in the post-processing stage can help with effective refinement of classification results.
Since 2016, which is regarded as the first year of the Era of Artificial Intelligence, the AI industry has been progressing rapidly with the fast development of mobile Internet and the massive generation of data. With data labeling the first key link of transferring human wisdom to machines or algorithms, the front end of building the AI industry chain is the need for a strong and powerful data labeling industry. The traditional data labeling industry is a labour-intensive industry that sells labour for cheap pay, and the data they label often does not belong to the labeling company itself, but to the data labeling commissioner. In this paper, we propose to build a data labeling industry centered on data trading, which is dedicated to transforming the data labeling company itself into a “big data company” to obtain ownership of the data. Thus, a large amount of labeled data for scenarios can be circulated through the “big data exchange” and other channels, promoting the rapid development of the overall artificial intelligence industry.
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