## 3 The Proposed Hyper-CycleGAN  
The proposed Hyper-CycleGAN architecture was designed to classify spectral information. In this architecture, the CycleGAN cycle network was adapted, with specific adjustments, as illustrated in Figure 1. This architecture comprises two streams: the target stream (indicated by orange lines) traverses from the target scene through the source scene and back, whereas the source stream (indicated by blue lines) follows a reverse path. Transforming original images into those of other scenes enables the generation of new hyperspectral data. However, it was observed that images generated by the original CycleGAN generator were marred by noise and poor quality, with a lack of detail. Consequently, the internal modules of the generator were modified, notably through the incorporation of a multi-scale attention mechanism. This mechanism enables the network to concentrate on various parts of the image at differing scales, thus more effectively capturing the details and features characteristic of hyperspectral images. Generator: Figure 2 depicts the proposed architecture, highlighting the internal configuration of the specially designed Hyper-CycleGAN generator module. Internally, the  
Figure 1: The framework of our proposed Hyper-CycleGAN. Hyper-CycleGAN generator's architecture employs an encoder-decoder framework consisting of three primary components: an encoder, a multi-scale attention module, and a decoder. The encoder functions by transforming the input image into an intermediary representation or feature vector, typically utilizing multiple convolutional layers to extract features at various levels within the image. Designed to capture inter-band correlations, the multi-scale attention module amplifies focus on specific spectral attributes and enhances understanding of spectral spatial information, thus improving the quality and precision of generated hyperspectral data. Conversely, the decoder component reverses the encoding process, translating the feature vector back into an image within the target domain through multiple inverse convolutional layers tasked with rendering the feature vector into the final image within said domain. Algorithm 1 delineates the pseudocode framework for the integration of a multi-scale attention module within the bespoke architecture of the Hyper-CycleGAN module, specifically devised for the synthesis of hyperspectral data. Our methodology is predicated on addressing the unique challenges intrinsic to hyperspectral data, by maintaining the original generator's capacity to discern and assimilate complex mapping relationships among hyperspectral images. This foundational principle is critical for ensuring the generator's ongoing effectiveness in enabling seamless transformations across diverse spectral domains. To elevate the quality and complexity of the synthesized hyperspectral data, we have introduced multi-scale attention modules. These modules are meticulously designed to enhance the generator's perceptual sharpness and contextual comprehension within the hyperspectral domain. Their design aims to identify correlations between varying wavelengths, focus attention on specific spectral features, and deepen the understanding of spectral spatial information. The intricate interplay between spectral bands is revealed through subtle variations and correlations across different wavelengths. Such interplay might include dependencies between adjacent spectral bands, illustrating how alterations in one band could affect the attributes of neighboring bands. Furthermore, spectral bands may engage in complex interactions, such as absorption and emission patterns, contributing to the scene's overall spectral signature. The emphasis on prominent spectral features within the multi-scale attention modules is evidenced by the selective amplification of specific spectral components identified as vital for the synthesis process. This entails focusing on distinctive spectral peaks or valleys that correlate with specific materials or substances in the scene.