TY - JOUR
T1 - Compression Supports Spatial Deep Learning
AU - Dax, Gabriel
AU - Nagarajan, Srilakshmi
AU - Li, Hao
AU - Werner, Martin
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - In the last decades, the domain of spatial computing became more and more data driven, especially when using remote sensing-based images. Furthermore, the satellites provide huge amounts of images, so the number of available datasets is increasing. This leads to the need for large storage requirements and high computational costs when estimating the label scene classification problem using deep learning. This consumes and blocks important hardware recourses, energy, and time. In this article, the use of aggressive compression algorithms will be discussed to cut the wasted transmission and resources for selected land cover classification problems. To compare the different compression methods and the classification performance, the satellite image patches are compressed by two methods. The first method is the image quantization of the data to reduce the bit depth. Second is the lossy and lossless compression of images with the use of image file formats, such as JPEG and TIFF. The performance of the classification is evaluated with the use of convolutional neural networks (CNNs) like VGG16. The experiments indicated that not all remote sensing image classification problems improve their performance when taking the full available information into account. Moreover, compression can set the focus on specific image features, leading to fewer storage needs and a reduction in computing time with comparably small costs in terms of quality and accuracy. All in all, quantization and embedding into file formats do support CNNs to estimate the labels of images, by strengthening the features.
AB - In the last decades, the domain of spatial computing became more and more data driven, especially when using remote sensing-based images. Furthermore, the satellites provide huge amounts of images, so the number of available datasets is increasing. This leads to the need for large storage requirements and high computational costs when estimating the label scene classification problem using deep learning. This consumes and blocks important hardware recourses, energy, and time. In this article, the use of aggressive compression algorithms will be discussed to cut the wasted transmission and resources for selected land cover classification problems. To compare the different compression methods and the classification performance, the satellite image patches are compressed by two methods. The first method is the image quantization of the data to reduce the bit depth. Second is the lossy and lossless compression of images with the use of image file formats, such as JPEG and TIFF. The performance of the classification is evaluated with the use of convolutional neural networks (CNNs) like VGG16. The experiments indicated that not all remote sensing image classification problems improve their performance when taking the full available information into account. Moreover, compression can set the focus on specific image features, leading to fewer storage needs and a reduction in computing time with comparably small costs in terms of quality and accuracy. All in all, quantization and embedding into file formats do support CNNs to estimate the labels of images, by strengthening the features.
KW - Deep learning
KW - image compression
KW - image quantization
KW - remote sensing image
KW - scene classification
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85144797893&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2022.3226563
DO - 10.1109/JSTARS.2022.3226563
M3 - Article
AN - SCOPUS:85144797893
SN - 1939-1404
VL - 16
SP - 702
EP - 713
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ER -