Published on: Mar 3, 2016
Transcripts - Nasa poster
Lunar Terrain ClassiﬁcationSean Brakken-Thal1, Jacqueline LeMoigne-Stewart2 Autonomous Landing Experiment Wavelet Transformation1:Washington Space Grant, University of Washington Wavelets are a family of functions that satisfy a set of certain mathematical properties. The2:NASA Goddard Space Flight Center Code 580 wavelet transformation is a procedure of taking a prototype function called the mother wavelet and creating a set of daughter wavelets. These daughter wavelets are scaled and translated copies of the mother wavelet. By then convolving these daughter wavelets with the original signal a Background set of wavelet coefﬁcients are created for each part of the signal. This process of transforming a signal into a set of wavelet coefﬁcients is referred to as the wavelet transformation.In 2004 NASA was given a new vision to return to exploration of the The Gabor Wavelet has had past success in image processing for iris recognition and ﬁngerprintsolar system. With the new vision they were given the challenge to re- recognition but little research has been done on using the Gabor Wavelet for terrain classiﬁcation.turn to the moon by 2018 with the hope that a mission to Mars would Figure 6: HiRISE and Range Cameras The Gabor wavelet is given by a harmonic function multiplied by a Gaussian function:be possible as well. To accomplish the goal of returning to the Moon, Currently missions to Mars and other objects in the solar system come at great risk due to theNASA created the Constellation Program to design the new generation i2π x +ψ x2 +γ 2 y 2 − 2σ2 inability to control the landers in real time. While NASA has the best landing procedures of any G=e λ eof space craft. The program consists mainly of the development of the other space administration much of the success of a landing comes down to luck. To increaseAres Launch Vehicles, the Orion Crew Vehicle and the Altair Lunar the reliability of landing it is suggested to place two cameras on a lander: A HiRISE camera for Instead of a complete expansion of the Gabor wavelet, a ﬁlter bank of several Gabor ﬁlters ofLander. In addition to the development of the next generation of space texture and edge detection and a LIDAR camera for height and slope detection. different dilation, orientation and phase is sufﬁcient for terrain classiﬁcation.vehicles is the necessity to develop the next generation of data process- Figure 1: Lunar Capsleing to aid in the ambitious goals to return to the solar system. This experiment tests the effectiveness of the two wavelets for texture classiﬁcation in conjunc- tion with edge detection, height classiﬁcation and slope detection for a possible lunar terrain.In data processing, image processing is one of the key areas under devel- The experiment is to guide a landing craft by utilizing texture and edgeopment. Texture segmentation will aid the future of the NASA’s explo- information from a HiRISE camera in conjunction to utilizing heightration aspirations with applications in long-range, mid-range and short- and slope information from a Range camera. The Range camera is used Figure 11: Gabor Filtersrange planning. For long-range planning texture segmentation will help to simulate a LIDAR camera.in landing site selection and path selection. For mid-range and short- One Variation of the Gabor wavelet is the Circular Gabor wavelet. This wavelet differs from therange planning texture segmentation has applications to autonomous To test the algorithms for landing and the different ﬁlters for the two traditional Gabor wavelet by being orientation invariant. Like the traditional Gabor wavelet it isprecision landing and obstacle avoidance. cameras a model of a possible lunar landscape was made. This model created by multiplying a sinusoid function with a Gaussian function: ˆ is then ﬁxed to a platform that is able to move in the z direction while √ x2 +y 2 x2 +γ 2 y 2 Figure 2: Ares I − 2σ2 ˆ ˆ the cameras are able to move in the x and y directions. By allowing the Figure 7: Lunar Model Gc = ei2π λ +ψ e cameras to move in the horizontal directions while the platform moves in the vertical a landing sequence is able to be tested. Shown below are some preliminary results of the HiRISE camera using the Gabor ﬁlter, the Circular Gabor ﬁlter and an edge detector. Figure 3: JSC Figure 12: Gabor Filters Texture Segmentation Olympus Mons ExperimentThe process of breaking the image into regions of like characteristics is known as segmenta- To test the effectiveness of the wavelet method a region of Martian terrain near Olympus Monstion. By segmenting the image into different regions further processing is able to be done to Figure 8: Gabor Filter Outputs of the Lunar Model (18◦N 133◦W ) was chosen to convolve with the Gabor and Circular Gabor ﬁlters. To simulateclassify a region based on its characteristics or to extract different features from the region. The Gabor ﬁlter showed moderate success with it interacting with the expected areas of texture. how the ﬁlters would operate during a landing sequence several resolutions of the terrain near Four ﬁlters were used to get the output shown which included two phases and two orientations. Olympus Mons were used. For the Gabor wavelet, 4 ﬁlters were used. For the Circular GaborOne type of image segmentation is to break the image into different re- wavelet one ﬁlter was used.gions based on the region’s texture. One of the underlying problems oftexture segmentation lies in how texture is deﬁned. The phenomenon oftexture is one that is frequently experienced but differs so greatly giventhe context of the experience that it is hard to deﬁne. One useful deﬁni- Figure 4: Texturetion is that a region of texture is a region of features, at some scale s0,such that when the region is viewed from a larger scale, s1, the region becomes homogeneous. Afeature is a region, at some scales0, such that when viewed at a smaller scale, s1, the region be-comes homogeneous. Thus by zooming out textures become homogeneous where as by zooming Figure 9: Circular Gabor Filter Output of the Lunar Modelin features become homogeneous. The Circular Gabor ﬁlter did not interact as well as the traditional Gabor ﬁlter though only oneA pixel of texture, then, would be one such that its neighborhood has a phase was tested. By only testing one phase the Circular Gabor ﬁlter took 25% of the computation Figure 13: Gabor Filter Outputslarge variance in the gray scale intensity of the image. Instead of calcu- time of the traditional Gabor Filter.lating the variance of every pixel’s neighborhood it is more efﬁcient toconvolve an image with a bank of ﬁlters that only interact strongly withregions of texture. This process of ﬁltering an image is called transform-ing the signal. This is done in hopes that texture in the ﬁltered image Figure 5: Featurewill be easier to detect and thereby classify as “Good” or “Bad”. Figure 10: Edge Detector Output of the Lunar Model The edge detector was successful in ﬁnding the edges in the image. To be used for landing zone Figure 14: Gabor Filter Outputs selection additional ﬁlters would be required due to its inability to interact with regions near an Figures 8 and 9 show a region near Olympus Mons, the Gabor and Circular Gabor ﬁlter outputs edge. and the outputs after thresholding.