# Multiple instance dense connected convolution neural network for aerial image scene classification

arXiv: https://arxiv.org/abs/1908.08156 · HTML: https://mlsanity.com/paper/1908.08156/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.08861/) (2022)
- [SurvNAM: The machine learning survival model explanation](https://mlsanity.com/paper/2104.08903/) (2021)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification](https://mlsanity.com/paper/2106.07879/) (2021)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images](https://mlsanity.com/paper/2211.14715/) (2022)
- [Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization](https://mlsanity.com/paper/2401.08860/) (2024)
- [A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification](https://mlsanity.com/paper/2203.04114/) (2022)
- [A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification](https://mlsanity.com/paper/2203.16325/) (2022)
- [Learning Instance Representation Banks for Aerial Scene Classification](https://mlsanity.com/paper/2205.13744/) (2022)
- [GOOD: Towards Domain Generalized Orientated Object Detection](https://mlsanity.com/paper/2402.12765/) (2024)

## Abstract (arXiv)

With the development of deep learning, many state-of-the-art natural image scene classification methods have demonstrated impressive performance. While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated. One possible solution is to preserve more local semantic information and enhance feature propagation. In this paper, an end to end multiple instance dense connected convolution neural network (MIDCCNN) is proposed for aerial image scene classification. First, a 23 layer dense connected convolution neural network (DCCNN) is built and served as a backbone to extract convolution features. It is capable of preserving middle and low level convolution features. Then, an attention based multiple instance pooling is proposed to highlight the local semantics in an aerial image scene. Finally, we minimize the loss between the bag-level predictions and the ground truth labels so that the whole framework can be trained directly. Experiments on three aerial image datasets demonstrate that our proposed methods can outperform current baselines by a large margin.