**A Particle Swarm Optimization Based Approach to PreȬtune Programmable Hyperspectral Sensors**

**Bikram Pratap Banerjee 1,2 and Simit Raval 2,\***


This paper designed an innovative workflow that can be implemented to simplify the process of in-field spectral sampling and its real-time analysis for the identification of optimal spectral wavelengths, specifically for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), which requires a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. The proposed band-selection optimization workflow involves particle-swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging in a given environment, where the criterion function, MEAC, greatly simplifies the infield spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with a diversity of vegetation species and communities. The optimal set of bands was found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This further reduces the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient and requires less data storage and computational resources for post-processing the data.

#### VII. **Hyperspectral Reconstruction (one paper)**

remotesensingȬ13Ȭ00115

### **Residual Augmented Attentional UȬShaped Network forȱ ȱ Spectral Reconstruction from RGB Images**

**Jiaojiao Li †, Chaoxiong Wu \*, Rui Song †, Yunsong Li and Weiying Xie**

The State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710000, China;ȱ ȱ jjli@xidian.edu.cn (J.L.); rsong@xidian.edu.cn (R.S.); ysli@mial.xidian.edu.cn (Y.L.); wyxie@xidian.edu.cn (W.X.) **\*** Correspondence: cxwu@stu.xidian.edu.cn; Tel.: +86Ȭ155Ȭ2960Ȭ9856

† These authors contributed equally to this work.

This paper proposed a deep residual-augmented attentional u-shape network (RA2UN) for spectral reconstruction (SR) using several double-improved residual blocks (DIRB) instead of paired plain convolutional units. Specifically, a trainable spatial augmented attention (SAA) module was developed to bridge the encoder and decoder to emphasize the features in the informative regions. Furthermore, a channel-augmented attention (CAA) module embedded in the DIRB was also introduced to adaptively rescale and enhance residual learning by using first-order and second-order statistics for stronger feature representations. Finally, a boundary-aware constraint was employed to focus on the salient edge information and recover more accurate high-frequency details. Experimental results on four benchmark datasets demonstrated that the proposed RA2UN network outperformed the state-of-the-art SR) methods in terms of quantitative measurements and perceptual comparison.

VIII. **Hyperspectral Image Visualization (one paper)**

remotesensingȬ12Ȭ02479Ȭv2
