# A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification

## TL;DR

This paper introduces a multi-attention system combining CNN and RNN for improved multi-label remote sensing image classification, demonstrating superior performance on a large-scale benchmark dataset.

## Contribution

It proposes a novel multi-attention framework with a multi-branch CNN and bidirectional RNN to enhance local descriptor modeling and spatial relationship capturing.

## Key findings

- Outperforms state-of-the-art methods on BigEarthNet dataset.
- Effectively models spatial relationships among image patches.
- Utilizes a novel patch-based multi-attention mechanism.

## Abstract

This paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed system consists of four main modules. The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial resolutions. To this end, we introduce a K-Branch CNN, in which each branch extracts descriptors of image bands that have the same spatial resolution. The second module aims to model spatial relationship among local descriptors. This is achieved by a bidirectional RNN architecture, in which Long Short-Term Memory nodes enrich local descriptors by considering spatial relationships of local areas (image patches). The third module aims to define multiple attention scores for local descriptors. This is achieved by a novel patch-based multi-attention mechanism that takes into account the joint occurrence of multiple land-cover classes and provides the attention-based local descriptors. The last module exploits these descriptors for multi-label RS image classification. Experimental results obtained on the BigEarthNet that is a large-scale Sentinel-2 benchmark archive show the effectiveness of the proposed method compared to a state of the art method.