Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification
نویسندگان
چکیده
The hyperspectral remote sensing images are classified by traditional neural networks methods can achieve promising performance, but only operate on regular square regions with fixed. This will lead to between neighborhood pixels have limitations in long-distances joint interaction modeling and cross-spacetime information flow for capturing complex spatial-temporal dependencies. Meanwhile ignoring importance detail improved utilization of irrelevant information. In the work, we propose a stack attention-pruning multiscale aggregates graph convolution framework (SAP-MAGACN). automatically learn selectively attend relevant subspace structure module, effectively disentangle space capture rich structural semantics. refine constructures. Then adopt aggregation manner nodes different effective long-range modeling. Finally, leverage dense edges completion propagation information, gradually produce discriminative embedded features distinguish categories boundary pixels. experimental results shown SAP-MAGCN outperformance all others state-of-the-art Indian Pines Salinas public benchmark datasets. Such as OA, AA Kappa our frameworks is 96.75%, 95.73% 97.33%, respectively,
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3061489