One of the most hazardous landing scenarios for rotorcraft is the pinnacle landing. During pinnacle landings the ight crews approach areas where traditional landings are not possible and place a portion of the aircraft on the ground (e.g. skids or back wheels) in order to extract or drop off passengers in challenging terrain. These landings are used for such operations such as air assault and mountain search and rescue. In these situations, the flight crew must perform precise maneuvers in close proximity to hazards with minimal visibility. Typically, crew members provide aural cues to the pilot to guide the aircraft. These maneuvers are proven to be demanding and have led to many accidents. Boeing is developing a sensor based pilot situational capability to aid the ight crews during pinnacle landings. We will present the design trade space and operational parameters that informed our approach. In addition, we will discuss the system development, testing and human factor considerations.
With the development of a myriad of imaging sensors and associated image processing algorithms to address the DVE problem relative performance evaluations have becoming increasingly important. In this paper, we introduce image quality metrics which have been selected for DVE applications. These quality metrics are based on the human visual system and consider factors such as induced processing noise, information content, and preserved image detail. These measures are shown to be useful for the evaluation of imaging sensors and associated processing. In addition, these measures provide direction for tuning and optimizing DVE local area processing (LAP) algorithms. Results will be shown for sample test images and dust trials of a camera with various image processing algorithms.
Multi-waveband infrared (IR) sensors capture more spectral information of atmospheric particles and may provide better penetration thru dust under dynamically changing conditions. Therefore, enhancing the visibility of multi-waveband infrared images obtained in degraded visual environment (DVE) is an important way to improve the perception of the environment under DVE conditions. In this paper, we present a system to enhance visibility in DVE conditions by modifying the wavelet coefficients of multi-waveband IR images. In the proposed system, input multi-waveband IR images are transferred into the wavelet domain using an integer lifting wavelet transformation. The low-frequency wavelet coefficients in each waveband are independently modified by an adaptive histogram equalization technique for improving the contrast of the images. To process high-frequency wavelet coefficients, a joint edge-mapping filter is applied to the multi-waveband high-frequency wavelet coefficients to find an edge map for each subband of wavelet coefficients; then a nonlinear filter is used to remove noise and enhance edge coefficients. Finally, the inverse lifting wavelet transformation is applied to the modified multi-waveband wavelet coefficients to obtain enhanced multiwaveband IR images. We tested the proposed system with degraded multi-waveband IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of multi-waveband IR images under DVE conditions.
The solution space to the DVE sensor problem can be considered to be a continuum where the goal is to minimize Size, Weight, Power, Cost (SWAP-C), and complexity while simultaneously maximizing performance. Performance is often achieved at the expense of SWAP-C and complexity. The core DVE sensor system technologies can be grouped into three broad areas: (1) sensors (e.g., LiDAR, radar, or imaging); (2) data processing such as fusion or sensor processing; and (3) synthetic vision, and symbology. Much of the body of DVE sensor research has focused on advancing the current state of the art in one or more of these particular areas, such as advanced sensing and/or data processing technologies. However, often the difficulties of integrating such a DVE technology into an aircraft and obtaining the proper hardware/software certification(s) for flight are not considered. Both of these adversely impact SWAP-C and complexity. In this paper we examine the solution space to the DVE sensor problem, identify the key drivers for SWAP-C and complexity, and present strategies for their mitigation.
Enhancing the visibility of infrared images obtained in a degraded visibility environment is very important for many applications such as surveillance, visual navigation in bad weather, and helicopter landing in brownout conditions. In this paper, we present an IR image visibility enhancement system based on adaptively modifying the wavelet coefficients of the images. In our proposed system, input images are first filtered by a histogram-based dynamic range filter in order to remove sensor noise and convert the input images into 8-bit dynamic range for efficient processing and display. By utilizing a wavelet transformation, we modify the image intensity distribution and enhance image edges simultaneously. In the wavelet domain, low frequency wavelet coefficients contain original image intensity distribution while high frequency wavelet coefficients contain edge information for the original images. To modify the image intensity distribution, an adaptive histogram equalization technique is applied to the low frequency wavelet coefficients while to enhance image edges, an adaptive edge enhancement technique is applied to the high frequency wavelet coefficients. An inverse wavelet transformation is applied to the modified wavelet coefficients to obtain intensity images with enhanced visibility. Finally, a Gaussian filter is used to remove blocking artifacts introduced by the adaptive techniques. Since wavelet transformation uses down-sampling to obtain low frequency wavelet coefficients, histogram equalization of low-frequency coefficients is computationally more efficient than histogram equalization of the original images. We tested the proposed system with degraded IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of degraded IR images.
Conference Committee Involvement (4)
Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations II
11 April 2021 | Orlando, Florida, United States
Situation Awareness in Degraded Environments 2020
27 April 2020 | Online Only, California, United States
Situation Awareness in Degraded Environments 2019
16 April 2019 | Baltimore, Maryland, United States