Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single-stream CNN with Learnable Architecture for Multi-source Remote
  Sensing Data   In this paper, we propose an efficient and generalizable framework based on
deep convolutional neural network (CNN) for multi-source remote sensing data
joint classification. While recent methods are mostly based on multi-stream
architectures, we use group convolution to construct equivalent network
architectures efficiently within a single-stream network. We further adopt and
improve dynamic grouping convolution (DGConv) to make group convolution
hyperparameters, and thus the overall network architecture, learnable during
network training. The proposed method therefore can theoretically adjust any
modern CNN models to any multi-source remote sensing data set, and can
potentially avoid sub-optimal solutions caused by manually decided architecture
hyperparameters. In the experiments, the proposed method is applied to ResNet
and UNet, and the adjusted networks are verified on three very diverse
benchmark data sets (i.e., Houston2018 data, Berlin data, and MUUFL data).
Experimental results demonstrate the effectiveness of the proposed
single-stream CNNs, and in particular ResNet18-DGConv improves the
state-of-the-art classification overall accuracy (OA) on HS-SAR Berlin data set
from $62.23\%$ to $68.21\%$. In the experiments we have two interesting
findings. First, using DGConv generally reduces test OA variance. Second,
multi-stream is harmful to model performance if imposed to the first few
layers, but becomes beneficial if applied to deeper layers. Altogether, the
findings imply that multi-stream architecture, instead of being a strictly
necessary component in deep learning models for multi-source remote sensing
data, essentially plays the role of model regularizer. Our code is publicly
available at https://github.com/yyyyangyi/Multi-source-RS-DGConv. We hope our
work can inspire novel research in the future.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg