DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zanetti et al. (A System for Burned Area Detection on Multispectral Imagery, 2022, IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, Pages 1-15), hereinafter “Zanetti”.
Regarding claim 1, Zanetti teaches:
A method of burnt area detection (See the Abstract.), comprising:
obtaining first infrared (IR) image data based on one or more first IR images of an earth surface (See page 4, section II.E: “Spectral Detail in NIR and SWIR: To maximize BA spectral representation on different scenarios and conditions such as vegetation types and different kinds of fire events such as wildfires or human-ignited fires, and land use practices. We account that Sentinel-2 and Landsat 8 provide similar spectral bands in the portion of the spectrum relevant for BA detection (cf. Table III).” Then see XL,tk-1 in Fig. 1 on page 4:
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Finally see page 4, section III: “As shown in Fig. 1, the proposed multitemporal system for BA detection is made of two components: 1) a multitemporal image composer and 2) a BA detector. The first module is deputed to the generation of a bitemporal image pair where the “post” image is the target image, whereas the “pre” image is a multitemporal composite of the most recent nonoccluded (clouds, shadows, and so on) pixel values available in a fixed period of time before the target date.” The most recent image in the composite meets the claimed “first infrared (IR) image data”.);
determining, as a hotspot area, an area on the earth surface with high thermal emission in the one or more first IR images, based on the first IR image data (See page 3, section III.D: “The BA detection phase divides into three steps: 1) candidate burned pixels detection;”. Then see page 6, section IV.B, 2nd paragraph: “The typical observation of BA presents low response in the visible range of the spectrum due to the presence of dark matter and stronger signal in the shortwave infrared region due to higher temperatures and the low evapotranspiration [33].” Also see page 11, left column, 1st full paragraph: “Essentially, false alarms can be characterized by typology of the land cover class being erroneously detected as burned.”);
obtaining second IR image data based on one or more second IR images of an area of the earth surface that at least partially overlaps with the hotspot area, wherein the one or more second IR images have been taken after the one or more first IR images (See XL, tk in Fig. 1:
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This image, emphasized in the reproduced figure, is taken after XL,tk-1. See the context on page 4, section III: “As shown in Fig. 1, the proposed multitemporal system for BA detection is made of two components: 1) a multitemporal image composer and 2) a BA detector. The first module is deputed to the generation of a bitemporal image pair where the “post” image is the target image, whereas the “pre” image is a multitemporal composite of the most recent nonoccluded (clouds, shadows, and so on) pixel values available in a fixed period of time before the target date.” Then see Fig. 3 on page 9, showing an example of the time series of images of Fig. 1, and each of the images depicts the same geographical region, meeting the claimed “at least partially overlaps with the hotspot area”.);
obtaining third IR image data based on one or more third IR images of an area of the earth surface that at least partially overlaps with the hotspot area, wherein the one or more third IR images have been taken before the one or more first IR images (See XL, tk-m+1 in Fig. 1:
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This image, emphasized in the reproduced figure, is taken before XL,tk-1. See the context on page 4, section III: “As shown in Fig. 1, the proposed multitemporal system for BA detection is made of two components: 1) a multitemporal image composer and 2) a BA detector. The first module is deputed to the generation of a bitemporal image pair where the “post” image is the target image, whereas the “pre” image is a multitemporal composite of the most recent nonoccluded (clouds, shadows, and so on) pixel values available in a fixed period of time before the target date.” Then see Fig. 3 on page 9, showing an example of the time series of images of Fig. 1, and each of the images depicts the same geographical region, meeting the claimed “at least partially overlaps with the hotspot area”.); and
determining a measure of a burnt area relating to the hotspot area based on the second IR image data and the third IR image data (See page 5, paragraph bridging the left and right columns, section III.D.2): “Therefore, after choosing a suitable spectral index XL,tsiss (spectral index for statistical-based screening), the full time series of nonoccluded values is analyzed as follows. For any candidate pixel (x,y), the spectral value XL,tksiss (x,y) is tested against its normality based on parameter estimates made with all nonoccluded spectral values measured between tk−m+1 and tk-1.”).
Regarding claim 2, Zanetti teaches:
The method according to claim 1, wherein the one or more first IR images are mid-wave IR images; and wherein the one or more second IR images and the one or more third IR images are short-wave IR images (See page 4, section II.E: “Spectral Detail in NIR and SWIR:”. Short-wave IR images are used. However, the examiner understands that there is overlap in bands between SWIR and mid-wave IR, since on page 6, section IV.B, 3rd paragraph, the SWIR channels are also used to calculate MIRBI (mid-infrared burned index): “by exploiting SWIR channels. The same bands are used in MIRBI, which was derived through linear regression as [35]”. The examiner asserts that each image in Fig. 1 includes short-wave and mid-wave IR bands, and so Zanetti meets “the one or more first IR images are mid-wave IR images” and “the one or more second IR images and the one or more third IR images are short-wave IR images”.).
Regarding claim 3, Zanetti teaches:
The method according to claim 1, wherein determining the measure of the burnt area relating to the hotspot area comprises: determining a measure indicative of a degree of vegetation burn for each pixel of a post-burn IR image that is based on the one or more second IR images and for each pixel of a pre-burn IR image that is based on the one or more third IR images (See the paragraph bridging pages 5-6, section III.D.2): “the system includes a step for the discrimination between strongly and weakly detected changed pixels in such a way the weak ones (the potential source of false alarms) can be eliminated from further analysis…Therefore, after choosing a suitable spectral index XL,tsiss (spectral index for statistical-based screening), the full time series of nonoccluded values is analyzed as follows. For any candidate pixel (x,y), the spectral value XL,tksiss (x,y) is tested against its normality based on parameter estimates made with all nonoccluded spectral values measured between tk−m+1 and tk-1.”).
Regarding claim 4, Zanetti teaches:
The method according to claim 3, wherein determining the measure of the burnt area relating to the hotspot area comprises: determining a difference image based on the pre-burn IR image and the post-burn IR image, wherein each pixel of the difference image has an assigned value that is based on a difference between the measure indicative of the degree of vegetation burn for a corresponding pixel of the pre-burn IR image and the measure indicative of the degree of vegetation burn for a corresponding pixel of the post-burn IR image (See Eq. 10 on page 6:
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).
Regarding claim 5, Zanetti teaches:
The method according to claim 4, wherein determining the measure of the burnt area relating to the hotspot area comprises: determining a burnt area in the area covered by the post-burn IR image based on the difference image (See page 5, section III.D.1): “Bitemporal indices(e.g., dNBR) are known to perform better than single date indices (e.g., NBR) on this task [32]. High values of bitemporal burn severity indices usually indicate for likely presence of burned matter. We therefore assume, as it is done inmost of the literature, that it is possible to discriminate between burned and unburned pixels according to thresholds set on the burned severity index values[13],[21],[30].”).
Regarding claim 6, Zanetti teaches:
The method according to claim 5, wherein determining the burnt area in the area covered by the post-burn IR image comprises applying a morphological snakes algorithm (See page 5, right column: “The map XL,tks can be viewed as a collection of seeds from which the full burned patches can be reconstructed…The reconstruction step aims to include less severely burned pixels spatially adjacent to the seeds according to an inclusion principle. The whole process is controlled by a constant that decides whether the inclusion is reliable or not and this is done at the level of connected components.”).
Regarding claim 7, Zanetti teaches:
The method according to claim 6, wherein a starting point for the morphological snakes algorithm is chosen inside the hotspot area (See page 5, right column: “The map XL,tks can be viewed as a collection of seeds from which the full burned patches can be reconstructed.).
Regarding claim 8, Zanetti teaches:
The method according to claim 5, further comprising rejecting false positives among determined burnt areas based on one or more of: an overlap with previously-determined burnt areas; a comparison of pixel values in the difference image to a threshold; and/or a landcover class of the burnt area (See page 1, right column, last paragraph: “To overcome well-known limitations of spectral-based methods such as false detections for certain types of land cover and missing detection of less severe burned spots, some mitigation techniques are proposed.” Then see page 11, left column, 1st full paragraph: “Essentially, false alarms can be characterized by typology of the land cover class being erroneously detected as burned.”).
Regarding claim 9, Zanetti teaches:
The method according to claim 5, further comprising determining a measure of confidence for a determined burnt area based on one or more of: a spectral separability for the determined burnt area determined based on the pre-burn IR image and the post-burn IR image; a number of hotspot areas other than the hotspot area to which the measure of the burnt area relates within the determined burnt area; and/or a spatial relationship of the determined burnt area to the hotspot area to which the measure of the burnt area relates (See page 3, left column, 2nd full paragraph: “The rationale of the algorithm is similar to that used for obtaining MODIS BA products, i.e., an initial BA map is created in the first step, from which tile-dependent statistics are extracted for the second step. In the first step, BA probability is computed over two consecutive Level-2A Sentinel-2 image products by means of mid-infrared burned index (MIRBI) and NBR2 spectral indices, and the NIR band.”).
Regarding claim 10, Zanetti teaches:
The method according to claim 1, further comprising: determining a plurality of areas on the earth surface with high thermal emission in the one or more first IR images (See page 3, section III.D: “The BA detection phase divides into three steps: 1) candidate burned pixels detection;”. Then see page 6, section IV.B, 2nd paragraph: “The typical observation of BA presents low response in the visible range of the spectrum due to the presence of dark matter and stronger signal in the shortwave infrared region due to higher temperatures and the low evapotranspiration [33].” Also see page 11, left column, 1st full paragraph: “Essentially, false alarms can be characterized by typology of the land cover class being erroneously detected as burned.”); and applying a clustering algorithm to the plurality of areas with high thermal emission to determine the hotspot area (See page 5, right column: “The reconstruction step aims to include less severely burned pixels spatially adjacent to the seeds according to an inclusion principle. The whole process is controlled by a constant that decides whether the inclusion is reliable or not and this is done at the level of connected components.”).
Regarding claim 11, Zanetti teaches:
The method according to claim 6, further comprising: determining an area of interest that includes and extends beyond the hotspot area by a predefined measure, wherein the area covered by the one or more second IR images and the area covered by the one or more third IR images each include the area of interest; and wherein the morphological snakes algorithm is applied to the area of interest (See page 5, right column: “The reconstruction step aims to include less severely burned pixels spatially adjacent to the seeds according to an inclusion principle.” The “seeds” meet the claimed “hotspot area”, and those “seeds” along with the included “less severely burned pixels spatially adjacent” meet the claimed “area of interest that includes and extends beyond”.).
Regarding claim 12, Zanetti teaches:
The method according to claim 11, further comprising: extending a boundary of the area of interest if at least part of the boundary of the area of interest is included in the burnt area (See page 5, right column: “The reconstruction step aims to include less severely burned pixels spatially adjacent to the seeds according to an inclusion principle.”).
Regarding claim 13, Zanetti teaches:
The method according to claim 1, further comprising: continuously obtaining the first IR image data for continuously determining hotspot areas on the earth surface (See page 8, paragraph bridging the left and right columns: “The core module of the proposed BA detection system provides a bitemporal-based BA detection map for a given date tk and a spatial frame L, the latter being a granule-orbit or path-row identifier in the case of Sentinel-2 or Landsat 8, respectively. This process is repeated for each image in the base time series on a spatial frame L.” A rolling window of frames appears to be analyzed in Fig. 1, which meets the claimed “continuously obtaining/determining”.).
Regarding claim 14, Zanetti teaches:
The method according to claim 1, further comprising: storing the one or more second IR images as third IR images after determining the measure of a burnt area relating to the hotspot area in the one or more first IR images (See page 8, paragraph bridging the left and right columns: “The core module of the proposed BA detection system provides a bitemporal-based BA detection map for a given date tk and a spatial frame L, the latter being a granule-orbit or path-row identifier in the case of Sentinel-2 or Landsat 8, respectively. This process is repeated for each image in the base time series on a spatial frame L.” Since a rolling window of frames appears to be analyzed in Fig. 1, the “post”/target image in Fig. 1 will eventually become one of the “pre” images.).
Regarding claim 15, Zanetti teaches:
The method according to claim 1, further comprising: maintaining a database for storing the third IR images (See the image database in Fig. 1 that stores all of the IR images.).
Zanetti teaches the apparatus of claim 16 for the reasons given in the treatment of claim 1.
Zanetti teaches the program (stored on a non-transitory computer-readable storage medium) of claim 17 for the reasons given in the treatment of claim 1.
Zanetti teaches the non-transitory computer-readable storage medium of claim 18 for the reasons given in the treatment of claim 1.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571)270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677
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