# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
###### Abstract
The Normalized Differential Vegetation Index (NDVI) proposed by [PERSON] uses the interaction between the red light (R) band and the near-infrared (NIR) band [13] to reflect the coverage of land vegetation; [PERSON] [14] use the soil regulation vegetation index, the normalized building index and the modified normalized water body index for constructing a new type of vegetation index called IVI (Index-based Vegetation Index), which can better suppress the influence of building land and water body information; [PERSON] is inspired by NDVI [15], using the interaction between the reflected near-infrared and visible green light to propose the Normalized Difference Water Index (NDWI) to describe the characteristics of open water and enhance its presence in remotely sensed digital images; [PERSON] proposed the Modified Normalized Difference Water Index (MNDWI) on the basis of NDWI [16], which takes advantage of the interaction between the green light (G) band and the near-infrared (NIR) band to better extract urban water information; the Multi-Band Water Index (MBWI) based on the characteristics of the water body index and the difference in reflectance between water body and low-reflective surfaces was constructed by [PERSON] [17], the research is based on the average spectral reflectance data of various types in the Qinhuai River Basin; [PERSON] [18] used the interaction between the near-infrared band and the short-wave infrared band to propose the Normalized Difference Barren Index (NDBI), whose effect is to extract residential area information; in the study of the change of land coverage, [PERSON] and [PERSON] [19] considered the lack of direct relationship between soil abundance and its spectral characteristics, it is difficult to identify soil features using remote sensing technology, so they used the interaction between the green light (G) band and the short-wave infrared band. They proposed a Normalized Difference Soil Index (NDSI), which can well extract soil information from vegetation and impervious surfaces; in order to reduce the potential agricultural losses, a new drought index IDI (Integrated Agricultural Drought Index) [20] integrating multiple indicators is proposed by [PERSON], which can effectively monitor the occurrence of drought events. Therefore, the interaction between wavebands plays a key role in the extraction of feature information from remotely sensed images, but how to automatically learn effective interaction when extracting different types of features is still a question worth exploring. The traditional method of extracting ground features through laboratory research and analysis is good for specific areas. However, it is difficult to directly apply to other areas because it often relies on expert experience and existing reference data [21]. With the development of machine learning and artificial intelligence, more and more researchers use machine learning methods to extract ground feature information. [PERSON] proposed method of a neural network based on Back Propagation (BP) to extract water automatically [22], this method can better extract water without setting a threshold; [PERSON] used the C5.0 decision tree method based on machine learning to extract wetland vegetation [23], and the effect was significant; [PERSON] integrated water index and regional FCM clustering method to extract urban surface water body [24] so that urban surface water boundaries have good regional integrity and local details.