Balanced Multi-Task Attention for Satellite Image Classification: A
  Systematic Approach to Achieving 97.23% Accuracy on EuroSAT Without
  Pre-Training
This work presents a systematic investigation of custom convolutional neural
network architectures for satellite land use classification, achieving 97.23%
test accuracy on the EuroSAT dataset without reliance on pre-trained models.
Through three progressive architectural iterations (baseline: 94.30%,
CBAM-enhanced: 95.98%, and balanced multi-task attention: 97.23%) we identify
and address specific failure modes in satellite imagery classification. Our
principal contribution is a novel balanced multi-task attention mechanism that
combines Coordinate Attention for spatial feature extraction with
Squeeze-Excitation blocks for spectral feature extraction, unified through a
learnable fusion parameter. Experimental results demonstrate that this
learnable parameter autonomously converges to alpha approximately 0.57,
indicating near-equal importance of spatial and spectral modalities for
satellite imagery. We employ progressive DropBlock regularization (5-20% by
network depth) and class-balanced loss weighting to address overfitting and
confusion pattern imbalance. The final 12-layer architecture achieves Cohen's
Kappa of 0.9692 with all classes exceeding 94.46% accuracy, demonstrating
confidence calibration with a 24.25% gap between correct and incorrect
predictions. Our approach achieves performance within 1.34% of fine-tuned
ResNet-50 (98.57%) while requiring no external data, validating the efficacy of
systematic architectural design for domain-specific applications. Complete
code, trained models, and evaluation scripts are publicly available.
68f6ea0d24c448936311180d
Aditya Vir
Balanced Multi-Task Attention for Satellite Image Classification: A
  Systematic Approach to Achieving 97.23% Accuracy on EuroSAT Without
  Pre-Training
This work presents a systematic investigation of custom convolutional neural
network architectures for satellite land use classification, achieving 97.23%
test accuracy on the EuroSAT dataset without reliance on pre-trained models.
Through three progressive architectural iterations (baseline: 94.30%,
CBAM-enhanced: 95.98%, and balanced multi-task attention: 97.23%) we identify
and address specific failure modes in satellite imagery classification. Our
principal contribution is a novel balanced multi-task attention mechanism that
combines Coordinate Attention for spatial feature extraction with
Squeeze-Excitation blocks for spectral feature extraction, unified through a
learnable fusion parameter. Experimental results demonstrate that this
learnable parameter autonomously converges to alpha approximately 0.57,
indicating near-equal importance of spatial and spectral modalities for
satellite imagery. We employ progressive DropBlock regularization (5-20% by
network depth) and class-balanced loss weighting to address overfitting and
confusion pattern imbalance. The final 12-layer architecture achieves Cohen's
Kappa of 0.9692 with all classes exceeding 94.46% accuracy, demonstrating
confidence calibration with a 24.25% gap between correct and incorrect
predictions. Our approach achieves performance within 1.34% of fine-tuned
ResNet-50 (98.57%) while requiring no external data, validating the efficacy of
systematic architectural design for domain-specific applications. Complete
code, trained models, and evaluation scripts are publicly available.
A novel balanced multi-task attention mechanism in custom convolutional neural networks improves satellite land use classification accuracy to 97.23% on the EuroSAT dataset without pre-trained models.
convolutional neural networks satellite land use classification EuroSAT dataset CBAM-enhanced balanced multi-task attention Coordinate Attention Squeeze-Excitation blocks learnable fusion parameter progressive DropBlock regularization class-balanced loss weighting Cohen's Kappa ResNet-50