Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Abstract

Compressive sensing (CS) technology is introduced into space optical remote sensing image acquisition stage, which could make wireless image sensor network node quickly and accurately obtain images in the case of two constraints of limited battery power and expensive sensor costs. On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction. FFPL-EDRNet consists of a convolution layer and a reconstruction network. We train FFPL-EDRNet end-to-end, thus greatly simplifying the pre-processing and post-processing process and eliminating the block effect of reconstructed images. The reconstruction network is based on residual network, which introduces multiscale feature extraction, multi-scale feature combination and multi-level feature combination. Feature fusion integrates low-level information with high-level information to reduce reconstruction error. The perceptual loss function based on pretrained InceptionV3 uses the weighted mean square error to define the loss value between the reconstructed image feature and the label image feature, which makes the reconstructed image more semantically similar to label image. In the measurement procedure, we use convolution to achieve block compression measurement, so as to obtain full image measurements. For image reconstruction, we firstly use a deconvolution layer to initially reconstruct the image and then use the residual network to refine the initial reconstructed image. The experimental results show that: in the case of measurement rates (MRs) of 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio (PSNR) = 27.502, 26.804, 24.593, 21.359 and structural similarity (SSIM) = 0.842, 0.816, 0.720, 0.568 of the reconstructed images obtained by FFPL-EDRNet. Therefore, Our FFPL-EDRNet could enhance the quality of image reconstruction.

Ship Detection Based on Compressive Sensing Measurements of Optical Remote Sensing Scenes

“…The CS measurements collected by LMM can obtain more image information, which is more beneficial for image reconstruction. Xiao et al [17] proposed fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet), which connects CML and reconstruction network for end-to-end training. LMM in this model can improve the image reconstruction quality in CS-based ORS imaging system.…”

“…Recently, there have been a lot of researches [15][16][17] To directly carry out ship detection on CS measurements, we adopt the method shown in Fig. 1(b) and design a CNN-based algorithm, CS-CenterNet, which achieves high-precision ship detection on CS measurements by jointly training the scene compression sampling phase and the measurements' ship detection phase.…”