# State of the Art of Remote Sensing Data: Gradient Pattern in Pseudocolor Composite Images

## TL;DR

This paper introduces an algorithm to detect gradient patterns in pseudocolor composite images from remote sensing data, enabling improved thematic image analysis.

## Contribution

A novel algorithm is proposed to identify gradient structures in pseudocolor images by transforming nominal spectral classes into a rank scale.

## Key findings

- The algorithm uses Moore neighborhoods and local binary patterns to detect gradient structures.
- Users can define criteria for gradient significance based on pixel statistics or key regions.
- Test examples demonstrate the algorithm's effectiveness in analyzing spatial structures.

## Abstract

The thematic processing of pseudocolor composite images, especially those created from remote sensing data, is of considerable interest. The set of spectral classes comprising such images is typically described by a nominal scale, meaning the absence of any predetermined relationships between the classes. However, in many cases, images of this type may contain elements of a regular spatial order, one variant of which is a gradient structure. Gradient structures are characterized by a certain regular spatial ordering of spectral classes. Recognizing gradient patterns in the structure of pseudocolor composite images opens up new possibilities for deeper thematic images processing. This article describes an algorithm for analyzing the spatial structure of a pseudocolor composite image to identify gradient patterns. In this process, the initial nominal scale of spectral classes is transformed into a rank scale of the gradient legend. The algorithm is based on the analysis of Moore neighborhoods for each image pixel. This creates an array of the prevalence of all types of local binary patterns (the pixel’s nearest neighbors). All possible variants of the spectral class rank scale composition are then considered. The rank scale variant that describes the largest proportion of image pixels within its gradient order is used as a final result. The user can independently define the criteria for the significance of the gradient order in the analyzed image, focusing either on the overall statistics of the proportion of pixels consistent with the spatial structure of the selected gradient or on the statistics of a selected key image region. The proposed algorithm is illustrated using analysis of test examples.