Efficient compression algorithm using learning networks for remote sensing images

Self Cite

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed.

Section:  =mentioning

confidence: 99%

Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis

Bai,

Qi,

Jin

et al. 2023

Self Cite

“…Compared to handcrafted transforms used in traditional image compression algorithms, learning-based algorithms can adapt to different characteristics of images [10]. In [40], researchers proposed a low-dimensional visual representation convolutional neural network for efficient remote sensing image compression. The network is used to transform coefficients in the wavelet domain from a large-scale representation to a smaller scale, obtaining an optimized wavelet representation by minimizing the distortion between the original and reconstructed wavelet representations.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Compression Based on the Multiple Prior Information

2023

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.

“…In the last six years, the use of Machine Learning (ML) has produced a breakthrough in lossy compression for natural images [4][5][6][7][8][9], surpassing techniques such as JPEG [10], JPEG 2000 [11], and intra-frame HEVC [12]. ML compression has also been applied to remote sensing data [13][14][15][16][17][18][19][20][21]. These contributions have employed models presented in [4,6] as a baseline architecture, and are focused on different types of data.…”

Section: Introductionmentioning

confidence: 99%

“…These contributions have employed models presented in [4,6] as a baseline architecture, and are focused on different types of data. For example, some authors adapted the ML architectures to compress monoband images with bit-depth higher than 8 bits [13,14,20], others expanded on the baseline architectures for multispectral sensors [15][16][17][18], and some applied them in compression of hyperspectral data [19,21].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning

Mijares i Verdú,

Ballé,

Laparra

et al. 2023

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images.