HunanMultimodalDataset
This is a multimodal remote‑sensing dataset for Hunan Province in 2017, comprising Sentinel‑2, Sentinel‑1, and SRTM data. It contains 400 training images (256×256), and 50 validation and test images. The TRI in the training set is computed from SRTM via GDAL and can be used for knowledge reconstruction.
Description
Dataset Overview
Dataset Name
HunanMultimodalDataset
Dataset Link
Dataset Content
- Year: 2017
- Location: Hunan Province, China
- Data Types: Multimodal remote‑sensing data, including Sentinel‑2, Sentinel‑1, and SRTM digital elevation data
Dataset Purpose
Used for land‑cover classification, especially with the proposed Domain Knowledge‑guided Deep Fusion Network (DKDFN).
Dataset Structure
- Training Set: 400 images of 256×256, includes TRI (derived from SRTM via GDAL)
- Validation & Test Sets: 50 images of 256×256 each
Data Processing
- Label Pre‑processing: Provided code converts IGBP classes to 0‑6 categories (0: cultivated land, 1: forest, 2: grassland, 3: wetland, 4: water, 5: unused land, 6: built‑up area).
Dataset Features
- Network Architecture: Multi‑head encoder with multi‑branch decoder supporting multitask learning (semantic segmentation and multimodal remote‑sensing index reconstruction).
- Loss Function: Proposed Asymmetric Loss Function (ALF) optimized for minority classes.
- Experimental Validation: Compared with six existing models (U‑Net, SegNet, PSPNet, DeepLab, HRNet, MP‑ResNet) demonstrating DKDFN’s superiority.
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Topics
Source
Organization: github
Created: 2/16/2022
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