Multi-Instance Multi-Label with Application to High Resolution Remote Sensing Images

Abstract

In this paper, we seek to solve the problem of remote sensing image classification using Multi Instance Multi Label Learning (MIML) framework, where each image contain multiple regions (instances) corresponds to multiple objects (labels). MIML framework is more efficient than traditional learning framework for complicated objects such as satellite images. Existing MIML algorithms such as MIMLboost, MIMLsvm and MIMLfast have been found useful in scene classification and can provide multiple labels for each instance in complicated objects. The proposed approach was performed in two steps: In the first step, we process the dataset using segmentation and feature extraction. Images are ambiguous because they contain numerous objects so we consider that each image is a bag and various blocks in the image are instances. In the second step, we apply MIML to classify each block of images. According to the experimental results, the proposed method outperforms the state-of-the-art methods.