Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (1803.02642v1)

Abstract: Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.

• The paper presents an end-to-end ReCNN that learns spectral-spatial-temporal features directly from multispectral data for enhanced change detection.
• By integrating CNNs for spatial-spectral extraction with RNNs for temporal modeling, the model outperforms traditional change detection techniques.
• Utilizing dilated convolutions, the network captures broader contextual details, enabling robust analysis of land cover changes in diverse scenarios.

Overview of the Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

The paper presents a distinctive architecture, termed Recurrent Convolutional Neural Network (ReCNN), tailored for change detection in multispectral imagery. This approach integrates the strengths of Convolutional Neural Networks (CNNs) for spectral-spatial feature extraction and Recurrent Neural Networks (RNNs) for temporal dependency modeling, achieving an end-to-end network capable of jointly encoding spectral, spatial, and temporal information.

Novel Contributions

The ReCNN architecture posits several novel contributions to multispectral image change detection:

1. End-to-End Learning Framework: In contrast to traditional methods that often require separate training phases or independent computation of components, ReCNN provides a seamless end-to-end trainable model. This eliminates the need for hand-crafted feature engineering, allowing the network to learn representations directly from raw multispectral data.
2. Integrated Temporal Model: Unlike typical change detection algorithms that rely on simple differencing or image stacking for temporal information, the ReCNN effectively harnesses temporal relationships through an RNN component. This integration is novel in the domain of multitemporal data analysis within remote sensing.
3. Dilated Convolution: To enhance the field of view without inflating the number of parameters, dilated convolutions are employed. This choice effectively captures broader contextual information, improving change detection accuracy while maintaining computational efficiency.

Experimental Evaluation

The ReCNN's performance was evaluated on two multispectral datasets: the Taizhou City dataset and the Eppalock Lake dataset. The paper reports competitive performance metrics when compared to conventional change detection methods such as CVA, PCA, MAD, IRMAD, SVM, and previous RNN-based approaches. Notably, ReCNN-LSTM, one variant of the architecture, consistently achieved higher overall accuracy (OA) and Kappa coefficients, indicating superior capability in modeling the intricate spectral-spatial-temporal dynamics of change phenomena.

Implications and Future Directions

This research carries significant implications both practically and theoretically for the field of remote sensing and beyond:

• Practical Impact: The ReCNN provides a robust tool for applications requiring precise land cover change detection, such as urban development monitoring, resource management, and environmental assessments. The demonstrated excellence in handling data from different geographical and contextual settings suggests it can generalize well across diverse remote sensing tasks.
• Theoretical Implications: The novel integration of CNN and RNN into a unified framework pushes the boundaries of deep learning applications in remote sensing. It sets a precedent for future architectures to handle multitemporal data more effectively, addressing challenges related to sequence modeling in spatially extended data.

Looking ahead, the expansion of this work could involve exploring semi-supervised learning techniques, enabling the network to utilize vast amounts of unlabeled multitemporal data more effectively. Additionally, adaptations of this framework to handle hyperspectral data or to integrate additional sources of remote sensing information (e.g., SAR data) could further enhance its utility and versatility in the ever-evolving landscape of geospatial analysis.

In conclusion, the paper contributes a significant advancement in change detection methodologies, setting the stage for future innovations leveraging deep learning frameworks in remote and geospatial sensing disciplines. The realization of such architectures paves the way toward more accurate, efficient, and scalable solutions for the modern challenges in monitoring Earth's dynamic systems.