Name
Title | ||
|---|---|---|
EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification. |
Abstract | ||
|---|---|---|
Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12010066 | REMOTE SENSING |
Keywords | Field | DocType |
attention-based weighting,convolutional neural network,high spatial resolution image,land-cover classification,multi-scale and multi-feature fusion,superpixel segmentation | Computer vision,Remote sensing,Artificial intelligence,Geology,Land cover,Image resolution | Journal |
Volume | Issue | Citations |
12 | 1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuyu Zhang | 1 | 0 | 0.34 |
| Chuanrong Li | 2 | 46 | 18.79 |
| shi qiu | 3 | 1 | 2.74 |
| Caixia Gao | 4 | 51 | 14.07 |
| Feng Zhang | 5 | 0 | 2.03 |
| Zhenhong Du | 6 | 31 | 16.98 |
| Liu Renyi | 7 | 15 | 13.13 |