Semi-Supervised Classification and Segmentation on High Resolution Aerial Images

TL;DR

This paper introduces a semi-supervised learning approach using pseudo labels to improve classification and segmentation of high-resolution aerial images from FloodNet, aiding post-disaster damage assessment.

Contribution

It presents a novel semi-supervised training method that incrementally incorporates pseudo labels to enhance model performance on limited labeled data.

Findings

Significant improvement over baseline supervised models.

Effective pseudo label generation for both classification and segmentation.

Enhanced generalization on validation and test sets.

Abstract

FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervised method of training helped us improve our baseline supervised loss by a huge margin for classification, allowing the model to generalize and perform better on the validation and test splits of the dataset. In this paper, we compare and contrast the various methods and models for image classification and semantic segmentation on the FloodNet dataset.