Domain-Adversarial Neural Networks for Deforestation Detection in Tropical Forests

Abstract

Many deep-learning-based, domain adaptation methods for remote sensing applications rely on adversarial training strategies to align features extracted from images of different domains in a shared latent space. However, the performance of such representation matching techniques is negatively impacted when class occurrences in the target domain, for which no labelled data is available during training, are highly imbalanced. In this work, we propose a deep-learning-based representation matching approach for domain adaptation in the context of change detection tasks. We further evaluate the approach in a deforestation mapping application, characterized by a high-class imbalance between the deforestation and no-deforestation classes. The domains represent different sites in the Amazon and Brazilian Cerrado biomes. To mitigate the class imbalance problem, we devised an unsupervised pseudolabeling scheme based on Change Vector Analysis that prevents the feature alignment to be biased towards the over-represented class. The experimental results indicate that the proposed approach can improve the accuracy of cross-domain deforestation detection.

Using Time Series Image Data to Improve the Generalization Capabilities of a CNN – The Example of Deforestation Detection With Sentinel-2

“…In the default setting, one considers a source domain (SD) for which plenty of training data is available and a target domain (TD) which is to be classified but for which an insufficient amount of training labels is available. It is also assumed that training in the SD only does not yield satisfactory performance in the TD due to the so called domain gap (Soto et al, 2022;Tasar et al, 2020).…”

“…Such techniques address the domain gap problem by transferring information from a labeled SD to an unlabeled TD. In particular, for deforestation detection, different methods based on domain adaptation have been proposed (Soto et al, 2022;Noa et al, 2021;Vega et al, 2021). The authors analysed the domain gap between several regions in the Amazon with specific time and geographical locations.…”

A Learning Strategy for Amazon Deforestation Estimations Using Multi-Modal Satellite Imagery

“…In the Amazon and Cerrado biomes, Ortega Adarme et al [41] evaluated patch-wise classification algorithms for the automatic detection of deforestation, finding that deep-learning-based approaches surpassed the SVM (support vector machine) [42] baseline on all performance metrics. In the context of the change detection of deforestation tasks, Soto et al [43] proposed a domain adaptation approach that takes into account several locations in the Amazon and Brazilian Cerrado biomes to increase the accuracy of cross-domain deforestation detection. De Andrade et al [44] extended the original DeepLabv3+ to solve class-imbalanced problems in deforestation detection in the Amazon using Landsat OLI-8 images.…”