● Hyperspectral Technology’s Application in Biomedical

The paper titled "Hyperspectral imaging and spectral-spatial classification for cancer detection," published in 2012, introduces hyperspectral imaging as an emerging technology for biomedical applications. The study proposes an advanced image processing and classification method for analyzing hyperspectral image data in prostate cancer detection. A least squares support vector machine (LS-SVM) is developed and evaluated for classifying hyperspectral data to enhance the detection of cancerous tissue. The method is applied to detect prostate cancer in tumor-bearing mice. Spatially resolved images are created to highlight the differences in reflectance characteristics between cancerous and normal tissues. Preliminary results in mice demonstrate that hyperspectral imaging and classification methods can reliably detect prostate tumors in animal models. Hyperspectral imaging technology provides a new tool for optical diagnosis of cancer.

In separate studies by Houzhu Ding et al. (2015) and Michael S. Chin et al. (2015), pigs and nude mice were used as experimental animals for hyperspectral imaging research on burn classification and recovery. The left image shows the distribution of oxygen saturation and hemoglobin in burn areas obtained through hyperspectral imaging analysis. T00, T01, T04, T24 represent the time points of 0 hours, 1 hour, 4 hours, and 24 hours after the burn, respectively. The right image consists of a color image of burned skin in nude mice, an image of oxygenated hemoglobin obtained through hyperspectral imaging analysis, and a tissue section. Hyperspectral imaging enables non-destructive, non-contact, and high-throughput grading of burn depth.

These studies demonstrate the potential of hyperspectral imaging in biomedical research, particularly in cancer detection and burn assessment, by leveraging the spectral and spatial information provided by this technology. The combination of hyperspectral imaging and advanced analysis methods offers valuable insights into disease diagnosis, monitoring, and treatment evaluation.

● Hyperspectral Technology’s Application in Biological Classification

The paper titled "Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach," published in 2013, investigated the spectral reflectance of three species of arboreal frogs using a dual-camera setup composed of two push-broom hyperspectral imaging systems. The systems generated reflectance images between 400 and 2500nm. The researchers analyzed the spectral reflectance of the frogs' skin.

All three species of tree frogs exhibited a visually observable green coloration. However, significant differences in spectral reflectance between the species were observed between 700 and 1100nm, allowing for species differentiation.

In this study, the dual hyperspectral imaging approach provided high spatial resolution and a wide spectral range for analyzing the frog skin reflectivity. The findings highlight the potential of hyperspectral imaging in non-invasively studying the spectral properties of frog skin, particularly in discerning different species based on their distinctive spectral reflectance patterns.

● Hyperspectral Technology’s Application in Archaeology of Cultural Heritage

Since the discovery of the Terracotta Warriors and Horses in 1974, they have garnered worldwide attention and have been hailed as the "Eighth Wonder of the World" by former French President Jacques Chirac. However, these precious cultural relics, including the Terracotta Warriors, have been buried underground for over two thousand years and are suddenly exposed to the air, making them highly susceptible to changes and posing significant challenges in terms of restoration and preservation.

Hyperspectral imaging technology enables the non-contact acquisition of image spectral information from the Terracotta Warriors. By analyzing the images and spectral information of the Terracotta Warriors, one can assess the extent of deterioration caused by diseases and determine the pigments used in their production. Based on the analysis results, simulated restoration can be performed.

The utilization of hyperspectral imaging technology in the restoration and preservation of the Terracotta Warriors provides valuable insights into their condition and assists in developing appropriate restoration strategies. This technology allows for non-invasive examination and documentation of the Terracotta Warriors' surface features, pigments, and deterioration patterns. By understanding the extent of damage and the original coloring materials, experts can make informed decisions regarding the restoration process.

The application of hyperspectral imaging technology not only contributes to the restoration of the Terracotta Warriors but also serves as a significant advancement in the field of cultural heritage preservation. It offers a scientific and efficient approach to studying and conserving cultural relics, ensuring their longevity and the transmission of historical and artistic values to future generations.

● Hyperspectral Technology’s Application in Fine Crop Classification and Recognition

Hyperspectral data can distinguish more subtle spectral differences in crops and detect changes in a narrower spectral range, enabling accurate detailed classification and information extraction of crops. Currently, the most popular and widely applied methods for hyperspectral crop classification include Spectral Angle Mapper (SAM) and Decision Tree-based hierarchical classification.

Based on hyperspectral imagery, Xiong Zhen from the Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences monitored the growth stages of rice in Changzhou. A hybrid decision tree method was employed for precise classification of rice varieties using hyperspectral images. The classification achieved an accuracy of 94.9% for distinguishing six rice varieties.

Zhang Bing considered the general rules of natural object distribution and utilized the features of massive hyperspectral remote sensing data. They employed an expert decision classification method optimized for spectral features to achieve fine classification of agricultural crops in Minamimaki, Japan, using hyperspectral imagery. The results demonstrated that this classification approach not only improved the accuracy of pixel classification but also significantly reduced misclassification noise in the resulting classification map.

● Hyperspectral Technology’s Application in Fine Crop Classification and Recognition

China is the world's largest grain-producing country, with grains including rice, wheat, corn, peanuts, and more. Through the application of hyperspectral imaging technology, acute detection of rice is conducted to assess its quality and variety. By obtaining hyperspectral images of rice, data in the images are processed using principal component analysis (PCA) to reduce dimensionality and extract characteristics such as whiteness and shape. Grain recognition models for rice are established using PCA and backpropagation neural network (BPNN). It is found that the BPNN model yields satisfactory results, with an accuracy of 89.91%, while PCA achieves an accuracy of 89.18%, with little difference between the two. By combining BPNN with data fusion, the accuracy is further improved to 94.45%. Therefore, the application of hyperspectral imaging technology for grain detection proves to be practical for rice variety and quality analysis.