Name
Title | ||
|---|---|---|
Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification. |
Abstract | ||
|---|---|---|
Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral-spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral-spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral-spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19071714 | SENSORS |
Keywords | Field | DocType |
hyperspectral image (HSI) classification,convolutional neural networks (CNNs),bidirectional LSTM,multi-scale features | Spatial analysis,Pattern recognition,Convolutional neural network,Image plane,Hyperspectral imaging,Electronic engineering,Correlation,Pixel,Artificial intelligence,Engineering,Deep learning,Spatial ecology | Journal |
Volume | Issue | ISSN |
19 | 7.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||