https://github.com/aniketdash7/cmsn_experiments-

Implementation of the CMSN model for cloud removal and experiments.
https://github.com/aniketdash7/cmsn_experiments-

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Implementation of the CMSN model for cloud removal and experiments.

• Host: GitHub
• URL: https://github.com/aniketdash7/cmsn_experiments-
• Owner: aniketDash7
• License: apache-2.0
• Created: 2025-04-14T17:19:23.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2025-04-14T17:26:27.000Z (about 2 months ago)
• Last Synced: 2025-04-14T18:29:29.035Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 1.39 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CMSN Cloud Removal Project Report

This report documents the implementation and experimentation of the CMSN (Coarse-to-refined multi-temporal synchronous cloud removal network) model as described in the referenced paper. The primary goal is to reconstruct cloud-free remote sensing images from multi-temporal, cloud-contaminated Landsat imagery using frequency and spatial domain learning.

---

## Setup Instructions (Steps to Run Notebook)

1. **Environment Setup**:

   - Google Colab or local Python environment with GPU recommended

   - Required libraries:

     ```bash

     pip install torch torchvision rasterio matplotlib earthengine-api scikit-image

     ```

2. **Google Earth Engine Authentication**:

   - Authenticate and initialize `ee` (Earth Engine Python API) in Colab

3. **Run Preprocessing Blocks**:

   - Define `regions`, date ranges, and import Landsat imagery

   - Apply QA-based cloud masking and create clipped patches

   - Export cloud-controlled patches to Google Drive

4. **Download Dataset**:

   - Use exported `.tif` files from Google Drive and organize them locally

   - Expected naming convention: `Region_T{1,2,3}_Patch{i}.tif`

5. **Model Training**:

   - Define CMSN architecture

   - Train using multi-temporal inputs with global-local, frequency, and refinement losses

   - Plot loss trends over 200 epochs

6. **Evaluation**:

   - Visualize model predictions

   - Calculate NDVI, PSNR, and SSIM for prediction quality

---

##  Dataset Preparation

- **Regions Used**:

  - `New South Wales, Australia`: 093/084

  - `Wuhan, China`: 123/039

- **Imagery Sources**:

  - Landsat 5 and 8 (TOA collections)

  - Spectral Bands: B2 (Blue), B3 (Green), B4 (Red), B5 (NIR)

- **Cloud Masking**:

  - Utilized `QA_PIXEL` band (bitwise flags: cloud, shadow, cirrus)

  - Controlled cloud levels: 25%, 15%, and 10% for T1, T2, and T3 respectively

- **Patch Extraction**:

  - Fixed-size 256x256 pixel patches at 30m resolution

  - Exported using `ee.batch.Export.image.toDrive()`

---

##  CMSN Model Architecture

- **ResFFTConv**:

  - Applies 2D FFT, followed by two convolutions + skip connection

  - Normalized frequency features and batch normalization added to stabilize gradients

- **MFAM (Multi-temporal Feature Aggregation Module)**:

  - Concatenates multi-temporal features and applies spatial attention via conv layers

- **CMSN Decoder & Refinement**:

  - Produces coarse reconstruction first

  - Applies one more conv layer with `tanh` to produce refined output

---

##  Loss Function (CMSNLoss)

- **Multi-temporal Global-Local Loss**: Preserves clear regions and reconstructs cloudy ones

- **Frequency Reconstruction Loss**: Uses 2D FFT to ensure frequency alignment

- **Refinement Loss**: Final L1 loss between refined output and ground truth

- Weighted combo: `loss = mtgl + delta * mtfr + xi * refine`

---

## Evaluation Metrics

- **NDVI (Normalized Difference Vegetation Index)**:

  ```python

  NDVI = (NIR - RED) / (NIR + RED + 1e-8)

  ```

  - Compared predicted NDVI vs ground truth

- **PSNR (Peak Signal-to-Noise Ratio)** and **SSIM (Structural Similarity Index)**:

  - Calculated between predicted and reference images using `skimage.metrics`

- **Visualization**:

  - RGB composite (B4, B3, B2 → Red, Green, Blue)

  - `t1`, `t2`, `t3`, `coarse`, `refined`, and `difference` plots

---

##  Challenges and Solutions

### 1. **NaN Values in Training**

- **Issue**: NaN loss during training due to image patches containing invalid pixels

- **Solution**: Replaced NaNs with 0 during preprocessing using `np.nan_to_num`

### 2. **Low Gradient Flow**

- **Issue**: Small gradient magnitudes (< 1e-3) hindered learning

- **Solution**: Introduced `BatchNorm2d` layers in `ResFFTConv` to stabilize training

### 3. **Color Distortion in Outputs**

- **Issue**: Output predictions appeared grey or washed-out

- **Solution**: Used natural color composite (bands B4, B3, B2) for visualization

### 4. **Incorrect NDVI Ranges**

- **Issue**: NDVI values > 1 or NaN due to unnormalized bands

- **Solution**: Normalized band values and clamped NDVI to [-1, 1]

### 5. **Loss Oscillation**

- **Issue**: Loss plateaued and oscillated after many epochs

- **Solution**: Tuned loss weights (`lambda_gl`, `delta`, `xi`) and learning rate

### 6. **FFT Input Shape Mismatch**

- **Issue**: Concatenated real and imag parts of FFT doubled input channels

- **Solution**: Used `abs()` of FFT instead of concatenation for shape compatibility

---

##  Suggestions for Future Work

- Add dropout to avoid overfitting

- Experiment with different optimizers (e.g., AdamW, Ranger)

- Increase dataset size by including more geographic regions (e.g., Tongchuan)

- Use attention modules or Swin Transformers for global context

- Evaluate on actual test data with real cloud-free targets

---

**Author**: Aniket  

**Project**: Cloud-aware Multi-temporal Satellite Network (CMSN)  

**Date**: April 2025