Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images

Alapfogalmak

A novel spatial-spectral reliable contrastive graph convolutional network (S2RC-GCN) is proposed to effectively classify complex land cover scenes using hyperspectral imagery.

Kivonat

The paper proposes a novel spatial-spectral reliable contrastive graph convolutional network (S2RC-GCN) for complex land cover classification using hyperspectral images.
Key highlights:

The model fuses spatial and spectral features extracted by 1D-CNN and 2D-CNN, with the 2D-CNN including an attention module to automatically extract important information.
The fused high-level features are used to construct graphs, which are then fed into GCNs to determine more effective graph representations.
A novel reliable contrastive graph convolution is proposed for reliable contrastive learning to learn and fuse robust features.
The model is evaluated on two complex land cover datasets captured by the Gaofen-5 satellite and a general hyperspectral dataset, demonstrating superior performance compared to other state-of-the-art methods.

The proposed S2RC-GCN effectively captures the spatial-spectral features of hyperspectral imagery and leverages reliable contrastive learning to improve the classification of complex land cover scenes.

Statisztikák

Spatial correlations between different ground objects are an important feature of mining land cover research.
Graph Convolutional Networks (GCNs) can effectively capture such spatial feature representations and have demonstrated promising results in performing hyperspectral imagery (HSI) classification tasks of complex land.
The Jiang Xia dataset captured by the GF-5 satellite comprises 330 bands with a spatial resolution of 30 m and 7 classes.
The Xin Jiang dataset captured by the GF-5 satellite has 7 classes.
The Salinas dataset captured by the AVIRIS sensor has 16 classes.

Idézetek

"To classify complex scenes more effectively, this study proposes a novel spatial-spectral reliable contrastive graph convolutional classification framework named S2RC-GCN."
"To better test the robustness and generalizability of the model, the paper uses two complex land cover datasets captured by the GF-5 and a general HSI dataset."

Főbb Kivonatok

S2RC-GCN

by Renxiang Gua... : arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00964.pdf

Mélyebb kérdések

How can the proposed S2RC-GCN framework be extended to incorporate additional modalities of data, such as LiDAR or SAR, to further improve the classification of complex land cover scenes

To extend the S2RC-GCN framework to incorporate additional modalities of data like LiDAR or SAR for improved classification of complex land cover scenes, a multi-modal fusion approach can be implemented. This involves integrating the features extracted from different data sources into a unified representation. For LiDAR data, which provides detailed elevation information, the framework can incorporate a separate branch that processes LiDAR data and fuses the extracted features with the spectral and spatial features. Similarly, for SAR data, which offers information on surface roughness and moisture content, a dedicated branch can be added to extract relevant features. These features can then be combined at a higher level to create a comprehensive representation that captures the unique characteristics of each modality. By leveraging the complementary nature of different data sources, the model can enhance its ability to classify complex land cover scenes accurately.

What are the potential limitations of the reliable contrastive learning approach used in the S2RC-GCN model, and how could it be further improved to handle noisy or ambiguous samples

While reliable contrastive learning in the S2RC-GCN model offers benefits in learning robust features, it may face limitations when handling noisy or ambiguous samples. One potential limitation is the reliance on high thresholds for label prediction, which could lead to the exclusion of valuable but uncertain samples from the contrastive learning process. To address this limitation, a more adaptive thresholding mechanism could be introduced, allowing the model to dynamically adjust the threshold based on the confidence of the predictions. Additionally, incorporating a mechanism for handling noisy samples, such as outlier detection or data augmentation techniques, can help improve the model's resilience to ambiguous or noisy data. By enhancing the model's ability to handle challenging samples, the reliable contrastive learning approach can be further improved to ensure robust performance in complex classification tasks.

Given the success of the S2RC-GCN model on complex land cover classification, how could the underlying techniques be adapted to address other challenging remote sensing tasks, such as change detection or object tracking

The success of the S2RC-GCN model in complex land cover classification tasks can be adapted to address other challenging remote sensing tasks like change detection or object tracking by leveraging the underlying techniques in a task-specific manner. For change detection, the model can be modified to compare features extracted from multi-temporal images to identify areas of change. By incorporating temporal information and adapting the graph construction process to capture changes over time, the model can effectively detect and classify changes in land cover. For object tracking, the model can be extended to track specific objects of interest by incorporating motion information and updating the graph representations dynamically. By integrating spatial-temporal features and designing a tracking mechanism within the GCN framework, the model can track objects across frames and scenes with high accuracy and efficiency. This adaptation demonstrates the versatility and scalability of the underlying techniques in the S2RC-GCN model for addressing a wide range of remote sensing tasks beyond land cover classification.