The availability of remote-sensing multisource data from optical-based satellite sensors has created new opportunities and challenges for forest monitoring in the Amazon Biome. In particular, change-detection analysis has emerged in recent decades to monitor forest-change dynamics, supporting some Brazilian governmental initiatives such as PRODES and DETER projects for biodiversity preservation in threatened areas. In recent years fully convolutional network architectures have witnessed numerous proposals adapted for the change-detection task. This paper comprehensively explores state-of-the-art fully convolutional networks such as U-Net, ResU-Net, SegNet, FC-DenseNet, and two DeepLabv3+ variants on monitoring deforestation in the Brazilian Amazon. The networks’ performance is evaluated experimentally in terms of Precision, Recall, F1-score, and computational load using satellite images with different spatial and spectral resolution: Landsat-8 and Sentinel-2. We also include the results of an unprecedented auditing process performed by senior specialists to visually evaluate each deforestation polygon derived from the network with the highest accuracy results for both satellites. This assessment allowed estimation of the accuracy of these networks simulating a process “in nature” and faithful to the PRODES methodology. We conclude that the high resolution of Sentinel-2 images improves the segmentation of deforestation polygons both quantitatively (in terms of F1-score) and qualitatively. Moreover, the study also points to the potential of the operational use of Deep Learning (DL) mapping as products to be consumed in PRODES.
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.
Abstract. Neural architecture search (NAS) is a subset of automated machine learning that tries to find the best neural network to perform a given task. In this article, a network search space is defined and applied to perform the semantic segmentation of satellite imagery. Due to the spatial nature of the data, the search space uses cells that group parallel operations with kernels of different sizes, providing options to accommodate the neighborhood information required to perform a better classification. The architecture search space follows a UNet-like network. The proposed approach uses scaled sigmoid gates, a strategy for network pruning that was adapted to search for the best operations on the cell search space. The architecture achieved by the proposed approach uses wider kernels on lower resolution feature maps, which leads to the interpretation that some pixels required information from pixels farther away than expected. The resulting network was compared to a very similar UNet-like network that only used 3×3 convolutions. The resulting network shows slightly better results on the test set.
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