CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

Technical Deep Dive: CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

Overview

CARL is a novel model for spectral image processing that enables camera-agnostic representation learning across diverse imaging modalities, including RGB, multispectral, and hyperspectral data. It addresses a key challenge in spectral imaging: the lack of a unified representation learning framework that can generalize across spectrally heterogeneous datasets.

Problem & Context

• Spectral imaging, including RGB, multispectral, and hyperspectral, captures enriched reflectance information in multiple wavelength channels.
• This enables a variety of applications in medicine, urban scene perception, and remote sensing.
• However, the evolution of spectral imaging technology has resulted in significant variability in camera devices, leading to the formation of camera-specific data silos.
• Existing models like CNNs and Vision Transformers cannot accommodate these spectral variations, resulting in camera-specific models with limited generalizability.
• Self-supervised pre-training has emerged as a powerful approach, but existing strategies are not camera-agnostic, restricting pre-training to camera-specific data silos.

Methodology

Camera-Agnostic Spectral Encoding

• CARL introduces a novel spectral encoder that transforms camera-specific spectral information into a camera-agnostic representation.
• It uses wavelength positional encoding to establish cross-camera channel correspondences, and learns spectral representations to efficiently encode the spectral dimension.
• The camera-agnostic representation is then processed by a standard spatial encoder like a Vision Transformer.

Camera-Agnostic Spatio-Spectral Self-Supervision

• CARL-SSL, a novel self-supervised learning framework, jointly learns camera-agnostic spatio-spectral representations.
• It includes a spectral self-supervision task that predicts masked spectral tokens, and a spatial self-supervision task that predicts masked spatial tokens.
• This enables CARL to leverage large-scale unlabeled datasets to learn robust representations.

Data & Experimental Setup

CARL was evaluated in three domains:

1. Medical imaging: Experiments on a private dataset of porcine organ hyperspectral images, with synthetically generated multispectral variants.
2. Automotive vision: Experiments on the Cityscapes RGB dataset and its hyperspectral counterpart, HSICity.
3. Satellite imaging: Experiments on a large corpus of Sentinel-2 multispectral and EnMAP hyperspectral data.

Results

• CARL demonstrated superior performance compared to both camera-specific and channel-invariant baselines across all three domains.
• It exhibited unique robustness to spectral heterogeneity in the medical imaging experiments, maintaining high accuracy as the training set was progressively contaminated with multispectral data.
• In the automotive experiments, CARL effectively leveraged RGB knowledge to improve hyperspectral segmentation, outperforming baselines.
• CARL's self-supervised pre-training also yielded the best average performance across 11 remote sensing benchmarks, including out-of-distribution datasets.

Interpretation

• CARL's camera-agnostic spatio-spectral encoding and self-supervision framework are key to its strong performance.
• By explicitly modeling wavelength information and learning enriched spectral representations, CARL can better align spectral signatures across different sensors.
• The combination of camera-agnostic pre-training and downstream fine-tuning enables effective cross-modal knowledge transfer.

Limitations & Uncertainties

• CARL has higher computational cost compared to channel-adaptive baselines.
• It may also struggle with sensor heterogeneity beyond just spectral properties, such as differences in spatial resolution.
• The current version does not handle sensor metadata beyond wavelength information.

What Comes Next

• Extending CARL to incorporate additional sensor metadata, such as spatial resolution, to further improve cross-sensor generalization.
• Investigating the scalability of CARL's self-supervised pre-training to even larger and more diverse spectral image datasets.
• Exploring the application of CARL's camera-agnostic representations to other downstream tasks beyond segmentation and classification.