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<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif;mso-ligatures:none"><img width="599" height="171" style="width:6.2395in;height:1.7812in" id="_x0000_i1037" src="cid:image001.png@01DBAEE8.8DC400C0" alt="Thesis Defense Announcement at the Cullen College of Engineering"><o:p></o:p></span></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C00000">A Unified Diffusion based Representation Learning Framework for Hyperspectral Image Analysis<o:p></o:p></span></b></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C00000"> </span></b><b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E"><br>
</span></b><b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none">Yuzhen Hu</span></b><span style="font-size:11.0pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">April 29, 2025, 1:00 p.m. to 3:00 p.m. (CST)<o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_MGVhZjFmODMtNTY0ZC00OGQ1LWE4MTYtNDYxZGM0NDFkYzdk*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*22c0a4c8cf-9aed-4850-a3b2-880cc2ea1c47*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!Cv6A-l8L4h-IT3Kc2XwSwVwrszNXbEZsxz13on1EX3VLIWtXnCtZjo2Dd45V-5sYnnxWDRHmECwvpsu94UM7lFPWrD4$">Teams
link</a> <o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Meeting ID: 287 765 081 841 0<o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Passcode: Vw9YG7LH<o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p> </o:p></span></b></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Saurabh Prasad, Ph.D. </span><span style="font-size:11.0pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Yashashree Kulkarni, Ph.D. | David Mayerich, Ph.D. </span><span style="font-size:10.5pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Hyperspectral imaging is a promising remote sensing modality for robust land-cover mapping
- however, analysis of such imagery is often challenging, owing to the high spectral dimensionality and limited pixel-level annotations for training. Additionally, hyperspectral imagery acquired from satellites are often lower resolution compared to their
multi-spectral or color-image counterparts.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Diffusion models exhibit strong generative capabilities and effectively preserve spatial
structure, making them well-suited for feature extraction from low spatial resolution hyperspectral imagery with degraded textures. We validate their efficacy by proposing an approach -- GeoDiffNet-F, which leverages pseudo-RGB representations and a diffusion
model pre-trained on natural images (ImageNet) without domain adaptation to extract transferable low-level spatial features. Combined with per-pixel spectral reflectance, these features significantly improve classification performance and outperform existing
baselines, highlighting the strength of diffusion-based spatial feature extraction in hyperspectral land-cover mapping. While GeoDiffNet-F demonstrates the utility of low-level features, the full potential of diffusion models lies in their ability to generate
hierarchical, tree-like representations through multi-step denoising—progressions ranging from global structures to fine details. Fully leveraging this capacity requires adaptation to the target domain. A central challenge is catastrophic forgetting, which
can degrade generalization from large-scale pretraining, especially under limited data. To address this, we propose a parameter-efficient domain-adaptive pre-training strategy for unsupervised representation learning, which updates only adaptive normalization
layers (e.g., FiLM-like affine modulation). This enables the model to extract modality-aware features and adapt rapidly while preserving general spatial priors.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Building on these insights, we introduce UniDiff-MM, a unified diffusion-based framework
that addresses the dual challenges of domain adaptation and multimodal fusion in hyperspectral imagery. UniDiff-MM combines two key innovations: (1) the above parameter-efficient adaptation strategy, and (2) modality-aware conditioning, where the diffusion
process is conditioned on distinct spectral views—pseudo-RGB for spatial and PCA-reduced bands for spectral content. This enables a single shared diffusion model to adapt to multiple domain-specific representations. It preserves modality-specific features
while aligning them through shared weights, projecting each view into a shared representation space. In practice, UniDiff-MM can generate modality-specific outputs when conditioned accordingly, demonstrating effective domain adaptation and consistent structure
across modalities.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">We validate UniDiff-MM on hyperspectral pixel-wise classification tasks, where it achieves
state-of-the-art performance and demonstrates its effectiveness for robust, multi-modal domain-adaptive representation learning.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><img border="0" width="599" height="82" style="width:6.2395in;height:.8541in" id="_x0000_i1038" src="cid:image002.png@01DBAEE8.8DC400C0" alt="Engineered For What's Next"></span><b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></b></p>
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