**Hyperspectral Imagery Classification Based on Multiscaleȱ ȱ SuperpixelȬLevel Constraint Representation**

**Haoyang Yu 1, Xiao Zhang 1, Meiping Song 1,\*, Jiaochan Hu 2, Qiandong Guo <sup>3</sup> and Lianru Gao <sup>4</sup>**


This paper developed a spectral–spatial classification method called superpixel-level constraint representation (SPCR) that uses the participation degree (PD) with respect to the sparse coefficient from the constraint representation (CR) model and then transforms the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) was further proposed through the decision fusion process of SPCR at different scales with the final decision of each test pixel determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the proposed methods.

#### II. **Hyperspectral Target Detection (three papers)**

remotesensingȬ12Ȭ01056Ȭv2

### **Underwater Hyperspectral Target Detection with Bandȱ ȱ Selection**

**Xianping Fu 1,2, Xiaodi Shang 1, Xudong Sun 1,2, Haoyang Yu 1, Meiping Song 1,\* and CheinȬI Chang 1,3,4**


This paper presented a fast, hyperspectral, underwater target-detection approach using band selection (BS). Due to the high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, the direct use of hyperspectral images in detecting targets cannot meet the needs of the rapid detection of underwater targets. To resolve this issue, the proposed method first developed constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset was constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm was used to detect the desired underwater targets. The experimental results demonstrated that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS could be significantly improved over the original underwater hyperspectral image system without BS.

remotesensingȬ13Ȭ04927Ȭv2
