Update README.md
Browse files
README.md
CHANGED
|
@@ -3,9 +3,9 @@
|
|
| 3 |
We created a synthetic dataset, RSyntHyperPDID, based on the AVIRIS database.
|
| 4 |
The dataset is sufficiently large to serve as a training set for hyperspectral dehazing models.
|
| 5 |
|
| 6 |
-
Official Dataset for HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark. [Preprints.org](https://www.preprints.org/manuscript/202510.1565)
|
| 7 |
|
| 8 |
-
Official Code: [GitHub](https://github.com/iitpvisionlab/hyperhazeoff).
|
| 9 |
|
| 10 |
### RGB/
|
| 11 |
For each split part (train/test) and clear hyperspectral image, two types of RGB visualizations are provided:
|
|
@@ -19,10 +19,65 @@ For each split part (train/test) and clear hyperspectral image, two types of RGB
|
|
| 19 |
|
| 20 |
|
| 21 |
### Hyperspectral Images (HSI)
|
| 22 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 23 |
|
| 24 |
## Dataset Structure
|
| 25 |
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 26 |
|
| 27 |
```
|
| 28 |
## Data Source
|
|
|
|
| 3 |
We created a synthetic dataset, RSyntHyperPDID, based on the AVIRIS database.
|
| 4 |
The dataset is sufficiently large to serve as a training set for hyperspectral dehazing models.
|
| 5 |
|
| 6 |
+
*Official Dataset for HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark.* [Preprints.org](https://www.preprints.org/manuscript/202510.1565)
|
| 7 |
|
| 8 |
+
*Official Code:* [GitHub](https://github.com/iitpvisionlab/hyperhazeoff).
|
| 9 |
|
| 10 |
### RGB/
|
| 11 |
For each split part (train/test) and clear hyperspectral image, two types of RGB visualizations are provided:
|
|
|
|
| 19 |
|
| 20 |
|
| 21 |
### Hyperspectral Images (HSI)
|
| 22 |
+
The RSyntHyperPDID dataset contains hyperspectral images of size 256×256 pixels with 182 spectral bands.
|
| 23 |
+
|
| 24 |
+
It is divided into three categories: *clear, train, and test*.
|
| 25 |
+
|
| 26 |
+
Each train and test sample includes hyperspectral images with synthetic haze, created based on a corresponding clear ground truth (GT) image.
|
| 27 |
+
|
| 28 |
+
The naming convention ensures the link between hazy and clear images:
|
| 29 |
+
- for example, **clear/XXXX.npy** serves as the GT for train/test samples named **XXXX_YYYY.npy**,
|
| 30 |
+
- where **XXXX** is the GT image identifier and **YYYY** represents haze synthesis parameters.
|
| 31 |
+
|
| 32 |
+
This naming scheme ensures straightforward pairing of hazy images with their clear counterparts for training and evaluation.
|
| 33 |
+
It supports reproducible experiments in haze removal and spectral image restoration.
|
| 34 |
+
|
| 35 |
+
Additionally, wavelength information for the hyperspectral bands is provided in the file *wavelengths_synthyper.npy*, supporting spectral analysis across the dataset.
|
| 36 |
|
| 37 |
## Dataset Structure
|
| 38 |
```
|
| 39 |
+
RSHPDID
|
| 40 |
+
|-- rgb
|
| 41 |
+
| |-- clear
|
| 42 |
+
| | |-- CSNC
|
| 43 |
+
| | | |-- 0001.png
|
| 44 |
+
| | | |-- ........
|
| 45 |
+
| | | `-- 0220.png
|
| 46 |
+
| | `-- CSSO
|
| 47 |
+
| | |-- 0001.png
|
| 48 |
+
| | |-- ........
|
| 49 |
+
| | `-- 0220.png
|
| 50 |
+
| |-- test
|
| 51 |
+
| | |-- CSNC
|
| 52 |
+
| | | |-- 0010_0030.png
|
| 53 |
+
| | | |-- .............
|
| 54 |
+
| | | `-- 0219_4040.png
|
| 55 |
+
| | `-- CSSO
|
| 56 |
+
| | |-- 0010_0030.png
|
| 57 |
+
| | |-- .............
|
| 58 |
+
| | `-- 0219_4040.png
|
| 59 |
+
| `-- train
|
| 60 |
+
| |-- CSNC
|
| 61 |
+
| | |-- 0001_0030.png
|
| 62 |
+
| | |-- .............
|
| 63 |
+
| | `-- 0220_4040.png
|
| 64 |
+
| `-- CSSO
|
| 65 |
+
| |-- 0001_0030.png
|
| 66 |
+
| |-- .............
|
| 67 |
+
| `-- 0220_4040.png
|
| 68 |
+
|-- clear
|
| 69 |
+
| |-- 0001.npy
|
| 70 |
+
| |-- ........
|
| 71 |
+
| `-- 0220.npy
|
| 72 |
+
|-- test
|
| 73 |
+
| |-- 0010_0030.npy
|
| 74 |
+
| |-- .............
|
| 75 |
+
| `-- 0219_4040.npy
|
| 76 |
+
|-- train
|
| 77 |
+
| |-- 0001_0030.npy
|
| 78 |
+
| |-- .............
|
| 79 |
+
| `-- 0220_4040.npy
|
| 80 |
+
`-- wavelengths_synthyper.npy
|
| 81 |
|
| 82 |
```
|
| 83 |
## Data Source
|