A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification

2022-11-05 · Cam Le, Lam Pham, Nghia NVN, Truong Nguyen, Le Hong Trang

In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image. In particular, we firstly evaluate different low complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2, NASNetMobile, and EfficientNetB0, which present the number of trainable parameters lower than 5 Million (M). After indicating best network architecture, we further improve the network performance by applying attention schemes to multiple feature maps extracted from middle layers of the network. To deal with the issue of increasing the model footprint as using attention schemes, we apply the quantization technique to satisfy the maximum of 20 MB memory occupation. By conducting extensive experiments on the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity model, which is very competitive to the state-of-the-art systems and potential for real-life applications on edge devices.

📄 PDF Abstract BibTeX arXiv:2211.02820

Code (0)

등록된 구현이 없습니다.

Tasks

image-classification Image Classification Quantization Remote Sensing Image Classification

Methods 이 논문이 사용한 방법론

Depthwise Convolution Depthwise Convolution is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D…

Pointwise Convolution Pointwise Convolution is a type of convolution that uses a 1x1 kernel: a kernel that iterates through every single point. This…

Depthwise Separable Convolution While standard convolution performs the channelwise and spatial-wise computation in one step, Depthwise Separable Convolution …

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Batch Normalization 설명 없음

RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification

2024-12-17 · Guangwenjie Zou, Liang Yao, Fan Liu, Chuanyi Zhang 외

Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model com…

Classification image-classification Image Classification Model Compression+2

RSMamba: Remote Sensing Image Classification with State Space Model

2024-03-28 · Keyan Chen, Bowen Chen, Chenyang Liu, Wenyuan Li 외

Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) …

Classification image-classification Image Classification Mamba+2

LSKNet: A Foundation Lightweight Backbone for Remote Sensing

2024-03-18 · YuXuan Li, Xiang Li, Yimian Dai, Qibin Hou 외

Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing classification, object detection and sema…

Change Detection object-detection Object Detection Object Detection In Aerial Images+1

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

2020-05-03 · Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo 외

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful fe…

Classification Deep Learning General Classification Generative Adversarial Network+2

RS-Mamba for Large Remote Sensing Image Dense Prediction

2024-04-03 · Sijie Zhao, Hao Chen, Xueliang Zhang, Pengfeng Xiao 외

Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transfo…

Building change detection for remote sensing images Change Detection Mamba Prediction+2