Name
Title | ||
|---|---|---|
Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data. |
Abstract | ||
|---|---|---|
Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a "dimension disaster". Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the "dimension disaster" caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12020321 | REMOTE SENSING |
Keywords | Field | DocType |
crop classification,polarimetric synthetic aperture radar (PolSAR),multi-temporal,stacked sparse auto-encoder (S-SAE),convolutional neural network (CNN) | Computer vision,Auto encoders,Artificial intelligence,Geology,Feature Dimension | Journal |
Volume | Issue | Citations |
12 | 2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiao Guo | 1 | 1 | 1.39 |
| Henghui Li | 2 | 0 | 0.68 |
| Jifeng Ning | 3 | 2 | 2.40 |
| Wenting Han | 4 | 0 | 0.34 |
| Wei-Tao Zhang | 5 | 28 | 5.67 |
| Zheng-Shu Zhou | 6 | 0 | 0.34 |