Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (1811.10166v2)

Abstract: New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth's surfaces. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS makes possible to monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not make the most of the temporal domain. Conversely, some approaches that take into account the temporal dimension have recently been tested, especially Recurrent Neural Networks (RNNs). This paper proposes an exhaustive study of another deep learning approaches, namely Temporal Convolutional Neural Networks (TempCNNs) where convolutions are applied in the temporal dimension. The goal is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification. This paper proposes a set of experiments performed on one million time series extracted from 46 Formosat-2 images. The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification. We also highlight some differences with results obtained in computer vision, e.g. about pooling layers. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size. Finally, we assess the visual quality of the land cover maps produced by TempCNNs.

• The paper demonstrates that TempCNNs significantly outperform traditional RF and RNN models by achieving 1–3% higher accuracy in land cover classification from satellite time series data.
• The paper employs detailed experiments with varying temporal and spectral guidance configurations, revealing that combined spectro-temporal convolution yields superior performance.
• The study highlights that pooling layers, especially global pooling, can reduce classification accuracy by omitting crucial temporal details, which advises careful neural network design.

Overview of Temporal Convolutional Neural Networks for Satellite Image Time Series Classification

This paper presents a comprehensive paper of Temporal Convolutional Neural Networks (TempCNNs) for the classification of Satellite Image Time Series (SITS). In the context of remote sensing, the authors investigate the efficacy of TempCNNs compared to traditional state-of-the-art algorithms such as Random Forests (RF) and advanced neural networks like Recurrent Neural Networks (RNNs). The paper emphasizes the potential improvements TempCNNs may bring to the classification accuracy of SITS and identifies critical considerations in TempCNN architecture choices, pooling strategies, and the integration of spectral-temporal insights.

Experimental Setup and Results

The authors evaluate TempCNN performance using an extensive dataset of one million time series derived from 46 Formosat-2 images, focusing on land cover classification. TempCNNs are shown to achieve 1 to 3 percent higher accuracy than RF and RNN models, which are traditionally utilized for SITS classification. The paper reports that the TempCNN architecture makes effective use of the temporal attributes in SITS, providing an advantage over other methods that may overlook temporal sequence nuances.

Several experiments were designed to dissect specific elements contributing to network performance. For instance, the authors analyze the role of temporal and spectral information by evaluating different configurations of TempCNNs: with no guidance, only temporal guidance, only spectral guidance, and combined spectro-temporal guidance. Their findings indicate that leveraging both temporal and spectral dimensions yields superior performance, highlighting the importance of multi-spectrum convolution applied across time.

Pooling Layers and Model Complexity

Contrary to some established practices in other fields, the paper reveals that incorporating pooling layers, particularly global pooling, can lead to decreased performance for SITS tasks. This outcome is attributed to the vital need for detailed temporal information to distinguish classes such as vegetation types, which diminishes by pooling. Consequently, this insight advises caution when implementing pooling technologies in SITS-focused neural networks.

The paper further explores the complexities of architectural configuration, such as depth and width impacts on the model. It shows that an optimal number of convolutional layers—typically two or three—achieves a better balance between bias and variance, ensuring high accuracy while managing variance with regularization techniques like dropout and weight decay. Moreover, the work underscores the vital role of batch normalization and dropout layers in controlling overfitting, especially in scenarios with considerably more parameters than training samples.

Practical and Theoretical Implications

From a practical perspective, the implications of this paper suggest that TempCNNs offer an efficient, scalable solution for creating high-accuracy land cover maps using SITS data. The insights into network architecture can guide practitioners in designing neural network models that optimally harness the temporal and spectral richness of data without unnecessarily complicating the model or computational demands. The pixel-wise classification capabilities shown in the paper can be pivotal for real-time environmental monitoring, informing policy-making related to disaster prevention, climate change tracking, and resource management.

Theoretically, this paper contributes to the body of knowledge by addressing the gap in how temporal aspects of SITS can be effectively modeled in neural networks. It sets the stage for further exploration into TempCNN models that might incorporate spatial information, adding another layer of complexity and opportunity for enhanced data interpretation.

Future Directions

The paper indicates several avenues for advancing this field. Further exploration of spatial integration via neural network architectures could yield even higher classification accuracies. The integration of spatial-temporal data in a cohesive model framework remains a challenging yet promising area. Furthermore, extending this research to evaluate newer satellite constellations, such as Sentinel-2, may offer additional insights and refine the understanding of TempCNN applicability and efficacy across different datasets and geographic conditions.

In conclusion, the research presented in this paper provides a detailed evaluation and advocacy for TempCNN models in SITS classification, highlighting their benefits over traditional methods and offering a guide on how to structure models for optimal performance.