SpanConv: A New Convolution via Spanning Kernel Space for Lightweight Pansharpening
Authors: Zhi-Xuan Chen, Cheng Jin, Tian-Jing Zhang, Xiao Wu, Liang-Jian Deng
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the proposed network significantly reduces parameters comparing with benchmark networks for remote sensing pansharpening, while achieving competitive performance and excellent generalization. 4 Experiments |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China, Chengdu, 611731 {zhixuan.chen, cheng.jin}@std.uestc.edu.cn, zhangtianjinguestc@163.com, wxwsx1997@gmail.com, liangjian.deng@uestc.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ zhi-xuan-chen/IJCAI-2022 Span Conv. |
| Open Datasets | Yes | All DL networks are trained and tested on World View-3 dataset with eight bands and Quick Bird dataset with four bands which are available on the public website2. 2https://www.maxar.com/product-samples/, https://earth.esa.int/eogateway/catalog/quickbird-full-archive |
| Dataset Splits | Yes | After downloading these datasets, we use Wald s protocol to simulate 10000 PAN/MS/GT image pairs with sizes of 64 64, 16 16 8, and 64 64 8, respectively, and divide them into 90%/10% for training (9000 examples) and validation (1000 examples). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or specific machine configurations) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We exploit the ℓ1 distance between the network prediction and the ground truth (GT) image to supervise the reconstruction process. Besides, the Adam optimizer is utilized with a learning rate that decays by 0.75 every 120 epochs. The initial learning rate and training period are 0.0025 and 800 epochs, respectively. |