# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
###### Abstract  
In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data. hyperspectral image; feature extraction; interactive features; sparse multiclass logistic regression; autonomous learning +
Footnote †: journal: MedicalThe initial extraction of land use and land cover information is achieved through manual visual interpretation, which places high professional requirements on operators. However, the operation speed is slow, thus effective information cannot be obtained in time. The rapid development of computer technology and the abundance of multiple sources of remotely sensed data have led to the emergence of a large number of automatic identification and classification methods [6]. Among them, the method of extracting remotely sensed information based on the spectral index of interaction has been widely used due to its simplicity and speed [7]. The concept of interaction is widely used in the discussion of statistics [8]. In most cases, the discussion is based on the familiar linear and quadratic statistics in the analysis of variance and linear regression. [PERSON] gave the definition of statistical interaction. The term \"statistical interaction\" is intended to indicate the interaction dependence between the influence of two or more factors within the scope of a given risk model [9]. From a statistical point of view, [PERSON] believes that if the individual effects of various factors do not add up, it can be said that an interaction has occurred, that is, the interaction is a special non-additive [10]. The common index of remotely sensed information is almost a kind of interaction between bands [11]. It is proposed based on ratio calculation and normalization processing. The purpose of ratio calculation is to eliminate the influence of terrain differences. The specific principle is to find the strongest and weakest reflection bands and place them in the positions of the numerator and denominator respectively. Through the ratio operation, the gap between the two can be further expanded in a geometric progression, to obtain the maximum brightness enhancement of the features on the image [12].