Performance Analysis of Deep Equilibrium Sparse SAR Imaging
Implementation Plan
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Step 1: Initially, we load and collect SAR image data from the “sentinel Dataset” .
Step 2: Next, we prepare input-output training pairs using geometric and system parameters based on collected data.
Step 3: Next, we implement a sparse SAR imaging model by applying compound regularization combining L1 norm (sparsity) .
Step 4: Next, we solve the imaging model using the Half-Quadratic Splitting (HQS) algorithm with alternating updates of auxiliary variables.
Step 5: Next, we implement a Deep Equilibrium (DEQ) network by defining a fixed-point iteration module using the HQS-based iterative function.
Step 6: Next, we train the model using CNN-based implicit constraints to optimize NMSE and PSNR.
Step 7: Finally, we plot performance for the following metrics:
7.1: Number of epochs vs. Accuracy (%)
7.2: Number of epochs vs. Precision (%)
7.3: Number of epochs vs. Recall (%)
7.4: Number of epochs vs. F1-score (%)
Software requirement:
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1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above
Dataset:
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Link :- https://www.kaggle.com/datasets/requiemonk/sentinel12-image-pairs-segregated-by-terrain
Note
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) If you have any changes in the Dataset , kindly provide before implementation.Our work is completely based on dataset values.
5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
matplotlib
pandas
scikit-image
