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On-board hyperspectral images are due to theirHigh spectral resolutionandLarge-scale spatial coverageHyperspectral targets have significant applications in tasks such as ground feature identification, urban monitoring, agricultural surveys, and environmental assessment. However, this type of data typically features numerous spectral bands, large target scale variations, complex backgrounds, and scarce labeled samples, making target detection tasks highly challenging. Traditional spectral matching and statistical feature methods struggle to simultaneously process spectral and spatial information, resulting in limited effectiveness for detecting small targets and complex backgrounds. In recent years, the development of deep learning technology has provided a new solution for on-board hyperspectral target detection. By automatically extracting spectral-spatial features through end-to-end training, it significantly improves the accuracy and robustness of detection.

Inland water bodies are an important component of the natural ecosystems upon which humankind depends for survival and development, playing an irreplaceable role in water supply, drought and flood control, transportation and navigation, climate regulation, providing habitats for organisms, and maintaining biodiversity. In recent years, under the influence of human activities and climate change, inland water resources, the water environment, and aquatic ecosystems have faced varying degrees of problems and threats. Eutrophication and the resulting algal blooms are prevalent pollution problems in inland water bodies, and the steady-state balance between watershed development and ecological coordination has gradually received attention from relevant departments.

The ocean covers 70% of the Earth's surface and is the largest natural ecosystem on Earth. Due to the complex and ever-changing marine environment, environmental monitoring and emergency early warning are crucial. Among these, the monitoring and forecasting of marine hydrological information using satellite remote sensing technology is vital for the development of marine resources, the growth of the marine economy, the protection of marine ecosystems, and the safeguarding of maritime rights.

Snow cover is a significant winter climate phenomenon in my country, impacting climate change, water resources, the ecological environment, and animal husbandry. However, due to my country's vast and complex topography, traditional accurate monitoring methods are limited by factors such as a small number of observation stations, uneven spatial distribution, discontinuous observation time, and insufficient information on regional snow cover distribution and changes. Today, with continuous technological advancements, satellite remote sensing has become an ideal tool for snow cover monitoring. Snow cover monitoring methods based on satellite remote sensing technology can accurately and efficiently obtain snow cover information for various regions, providing crucial support for my country's ecological environment management and animal husbandry development.

Wetlands possess vital ecological functions, including regulating climate, maintaining biodiversity, purifying water, and regulating floods, and are an important component of the global carbon cycle. Over the past century, under the dual pressures of climate change and human activities, the global wetland area has drastically decreased, with ecosystem structures being damaged, functions declining, and even lost. In particular, the spatial extent of wetlands in China has shrunk significantly. Ensuring the sustainable development of wetland environments and resources has become a critical issue.

With the development of industry and commerce, dense population, and rapid urbanization, the environmental burden on my country's water resources is increasing year by year. As a large-scale and efficient means of Earth observation, satellite remote sensing technology can play an important role in water quality monitoring, obtaining more accurate and comprehensive water quality results.

Vegetation plays a vital role in the Earth system, serving as an important renewable resource within the Earth's surface. As one of the most active and valuable influencing factors and indicators of global change, it simultaneously affects the energy balance of the Earth-atmosphere system and plays a crucial role in climate, hydrology, and biochemical cycles.

In the operation of remote sensing satellites, the main function is to use sensors mounted on remote sensing platforms to record the electromagnetic wave information emitted or reflected by target objects, thereby forming remote sensing images. Among these, resolution, as a measure of the sensor imaging system's ability to distinguish details in the output image, is an important technical indicator of the application value of remote sensing images. Different measures of "image detail" result in various types of resolution, mainly including spatial resolution, spectral resolution, temporal resolution, and radiometric resolution.