Deep Feature Clustering for Remote Sensing Imagery Land Cover Analysis

Abstract

In this letter, we propose a chip-based technique for large-scale, automatic land cover clustering of high-resolution remote sensing imagery (HR-RSI) using deep visual features from a deep convolutional neural network (DCNN) along with the fuzzy c-means algorithm. The proposed method, unlike traditional methods, facilitates utilizing transfer learning techniques for deep neural models that are fined-tuned to satellite imagery for feature extraction. Then, a large conterminous region of imagery is acquired from HR-RSI data providers and scanned to extract visual features utilizing the transfer learned model. Broad-area thematic and contextual understanding of the geographic land cover is efficiently achieved using feature reduction, chip-based clustering analysis, and geospatial rendering of the clusters. We explore a variety of fuzzy clusterings and their resulting utility for spatial analysis. The spatial densities of the clusters, numerical analysis, and geographic aggregations show that our proposed approach is effective in examining the land cover of the earth.