Improving Deforestation Detection Accuracy in Noisy Satellite Images with Contrastive Learning-based Approach

Abstract

Deforestation, the large-scale destruction of trees, has far-reaching biological and environmental consequences that pose a significant threat to the environment. Accurate deforestation detection is crucial for successful conservation initiatives and effective land management. Over the last decade, numerous deforestation detection methods utilizing spaceborne photography have been proposed. However, these methods tend to be sensitive to unique image noise in the satellite domain by virtue of the diverse aerial characteristics and air qualities in different regions. To solve this problem, we propose a novel noise-robust deforestation detection framework with a contrastive-learning based approach. The proposed framework consists of two phases: contrastive learning, which aims to extract similar feature embeddings for the same category, proceeded with transfer learning in order to develop the deforestation classifier. Remarkably, the proposed contrastive learning approach successfully handles noisy input satellite images during the feature extraction process. Upon conducting validation, we have found that the proposed method outperforms existing deforestation detection methods by a significant performance gap, highlighting the effectiveness of the proposed contrastive learning approach.