HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×.

A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation

In this paper, we present several study cases focused on marine, oceanographic, and atmospheric environments, which would greatly benefit from the use of a deployable system for small satellite observations. As opposed to the large standard ones, small satellites have become an effective and affordable alternative access to space, owing to their lower costs, innovative design and technology, and higher revisiting times, when launched in a constellation configuration. One of the biggest challenges is created by the small satellite instrumentation working in the visible (VIS), infrared (IR), and microwave (MW) spectral ranges, for which the resolution of the acquired data depends on the physical dimension of the telescope and the antenna collecting the signal. In this respect, a deployable payload, fitting the limited size and mass imposed by the small satellite architecture, once unfolded in space, can reach performances similar to those of larger satellites. In this study, we show how ecology and Earth Observations can benefit from data acquired by small satellites, and how they can be further improved thanks to deployable payloads. We focus on DORA—Deployable Optics for Remote sensing Applications—in the VIS to TIR spectral range, and on a planned application in the MW spectral range, and we carry out a radiometric analysis to verify its performances for Earth Observation studies.

Fast Method for Liquid Crystal Cell Spatial Variations Estimation Based on Modeling the Spectral Transmission

Liquid crystal phase retarders are utilized by photonic devices and imaging systems for various applications, such as tunable filtering, light modulation, polarimetric imaging, remote sensing and quality inspection. Due to technical difficulties in the manufacturing process, these phase retarders may suffer from spatial non-uniformities, which degrade the performance of the systems. These non-uniformities can be characterized by measuring the spectral transmission at each voltage and each point on the liquid crystal cell, which is time consuming. In this work, we present a new fast and simple method for measuring and computationally estimating the spatial variations of a liquid crystal phase retarder with planar alignment. The method is based on measuring the spectral transmission of the phase retarder at several spatial locations and estimating it at others. The experimental results show that the method provides an accurate spatial description of the phase retarder and can be employed for calibrating relevant systems.

Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information

The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulates an MRF model while grayscale and gradient statistical information of a multimodal image is employed for the evaluation of similarity while the spatial weighting function is optimized simultaneously. Besides, the value space is discretized to improve the convergence speed. The developed automatic approach was validated both qualitatively and quantitatively, demonstrating its potential for a variety of multimodal remote sensing datasets and scenes. As for the registration accuracy, the average target registration error of the proposed framework is less than 1 pixel, while the maximum displacement error is less than 1 pixel. Compared with the polynomial model registration based on manual selection, the registration accuracy has been significantly improved. In the meantime, the proposed approach had the partial applicability for the multimodal image registration of large deformation scenes. It is also proved that the proposed registration framework using grayscale and gradient information outperforms the MRF-based registration using only grayscale information and only gradient information while the proposed registration framework using Gaussian function as spatial weighting function is superior to that using distance inverse weight method.