Snow and glaciers are important sources of sustainable indicators for the availability of natural resources like freshwater, energy, minerals, forest, and agricultural products. Glaciers are disappearing, and snow cover areas are decreasing in the Himalayas due to increasing global temperatures and other related sensations. This study has been done using Hyperion imagery to identify different types of seasonal snow and glacier features in the north-western Himalayan states of India. Minimum noise fraction (MNF), Pixel Purity Index (PPI), and n-Dimensional (n-D) visualizer were applied to the atmospherically corrected Hyperion image. The spectral unmixing algorithm was applied to collect the endmembers or pure pixels from the Hyperion image. These endmembers were used for image classification. Different snow and glacier facies such as clean snow, ice mixed debris, blue ice, refreezing ice, dirty snow, dirty glacier ice, firn, moraine, glacier ice, and water body were found out using advanced pixel-based classification techniques. The field visits were conducted two times in a year from 2017 to 2019 in different parts of Himachal Pradesh, India. Sentinel-2 satellite data were used for accuracy assessment of the classification maps. Three advanced classifier methods artificial neural network (ANN), support vector machines (SVM), and random forest (RF) were used for the classification of Hyperion image. The overall accuracy for the classification algorithms ANN, SVM, and RF is 81.14%, 87.27%, and 90.98% respectively. These classification methods prove to be beneficial for classification of snow and ice properties for remote locations which are difficult to access and have rough weather conditions. However, appropriate field investigations and analyses using multiple satellite dataset are useful to increase the reliability of the results obtained from image classification.

雪和冰川是淡水、能源、矿产、森林和农产品等自然资源可用性可持续指标的重要来源。由于全球气温升高和其他相关感觉，冰川正在消失，喜马拉雅山脉的积雪面积正在减少。这项研究是使用 Hyperion 图像完成的，以确定印度西北部喜马拉雅各邦的不同类型的季节性雪和冰川特征。最小噪声分数 (MNF)、像素纯度指数 (PPI) 和 n 维 (nD) 可视化器应用于大气校正的 Hyperion 图像。应用光谱解混算法从 Hyperion 图像中收集端元或纯像素。这些端元用于图像分类。不同的雪和冰川相如干净的雪，使用先进的基于像素的分类技术发现了冰混合碎片、蓝冰、重新冻结的冰、脏雪、脏冰川冰、冷杉、冰碛、冰川冰和水体。从2017年到2019年，每年在印度喜马偕尔邦的不同地区进行了两次实地考察。Sentinel-2 卫星数据用于评估分类图的准确性。三种先进的分类方法人工神经网络（ANN）、支持向量机（SVM）和随机森林（RF）被用于Hyperion图像的分类。分类算法 ANN、SVM 和 RF 的总体准确率分别为 81.14%、87.27% 和 90.98%。这些分类方法被证明有利于对难以进入且天气条件恶劣的偏远地区的冰雪特性进行分类。然而，使用多个卫星数据集进行适当的实地调查和分析有助于提高图像分类结果的可靠性。