CS550 - Machine Learning : SAR Image Colorization

Code for Paper "SAR Image Colorization"

SAR (Synthetic Aperture Radar) provides terrain structural information. These information are in the form of greyscale images which has a lot of noise.

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HandBrake 1.5.1 (2022011000) OS: Microsoft Windows NT 10.0.19044.0 CPU: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz Ram: 16342 MB, GPU Information: NVIDIA GeForce GTX 1060 6GB - 30.0.15.1123 Screen: 1920x1080 Temp Dir: C:\Users\Andre\AppData\Local\Temp\ Install Dir: C:\Users\Andre\Desktop\Encode Anime Eater\HandBrake\ Data Dir: C:\Users\Andre\AppData\Roaming\HandBrake ------------------------------------------- # Starting Encode ... [17:03:35] base preset: Very Fast 1080p30 [17:03:36] Remote Process started with Process ID: 15644 using port: 8037. Max Allowed Instances: 1 [17:03:36] Worker: Starting HandBrake Engine ... [17:03:36] Worker: Starting Web Server on port 8037 ... [17:03:36] Worker: Disconnected worker monitoring enabled! [17:03:36] Compile-time hardening features are enabled [17:03:37] hb_init: starting libhb thread [17:03:37] Starting work at: Mon Jan 31 17:03:37 2022 [17:03:37] 1 job(s) to process [17:03:37] json job: { "Audio": { "AudioList": [ { "Bitrate": 160, "DRC": 0, "Encoder": "av_aac", "Gain": 0, "Mixdown": 4, "NormalizeMixLevel": false, "Samplerate": 0, "Track": 0, "DitherMethod": 0 } ], "CopyMask": [ "copy:aac", "copy:ac3", "copy:dtshd", "copy:dts", "copy:eac3", "copy:flac", "copy:mp3", "copy:truehd", "copy:mp2" ], "FallbackEncoder": "ac3" }, "Destination": { "ChapterList": [ { "Name": "Chapter 1" } ], "ChapterMarkers": true, "AlignAVStart": true, "File": "C:\\Users\\Andre\\Videos\\[Gm-Team][\u56FD\u6F2B][\u96EA\u9E70\u9886\u4E3B][Xue Ying Ling Zhu][01][\u950B\u8292\u521D\u9732][1080P] 1.mp4", "Mp4Options": { "IpodAtom": false, "Mp4Optimize": false }, "Mux": "av_mp4" }, "Filters": { "FilterList": [ { "ID": 4, "Settings": { "mode": "7" } }, { "ID": 3, "Settings": { "block-height": "16", "block-thresh": "80", "block-width": "16", "filter-mode": "1", "mode": "0", "motion-thresh": "2", "spatial-metric": "2", "spatial-thresh": "3" } }, { "ID": 13, "Settings": { "crop-bottom": "138", "crop-left": "0", "crop-right": "0", "crop-top": "138", "height": "804", "width": "1920" } }, { "ID": 6, "Settings": { "mode": "2", "rate": "27000000/900000" } } ] }, "PAR": { "Num": 1, "Den": 1 }, "Metadata": {}, "SequenceID": 0, "Source": { "Angle": 1, "Range": { "Type": "chapter", "Start": 1, "End": 1 }, "Title": 1, "Path": "D:\\[GM-Team][\u56FD\u6F2B][\u96EA\u9E70\u9886\u4E3B][Xue Ying Ling Zhu][01-26][1080P]\\[GM-Team][\u56FD\u6F2B][\u96EA\u9E70\u9886\u4E3B][Xue Ying Ling Zhu][01][\u950B\u8292\u521D\u9732][1080P].mp4" }, "Subtitle": { "Search": { "Burn": false, "Default": false, "Enable": true, "Forced": true }, "SubtitleList": [ { "Burn": true, "Default": false, "Forced": false, "ID": 0, "Offset": 0, "Track": -1, "Import": { "Codeset": "UTF-8", "Filename": "C:\\Users\\Andre\\Desktop\\[GM-Team][\u56FD\u6F2B][\u96EA\u9E70\u9886\u4E3B][Xue Ying Ling Zhu][01][\u950B\u8292\u521D\u9732][1080P].ass", "Language": "ron", "Format": "SSA" } } ] }, "Video": { "Encoder": "x264", "Level": "4.0", "TwoPass": false, "Turbo": false, "ColorMatrixCode": 0, "Options": "", "Preset": "veryfast", "Profile": "main", "Quality": 24, "QSV": { "Decode": false } } } [17:03:37] CPU: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz [17:03:37] - Intel microarchitecture Skylake [17:03:37] - logical processor count: 8 [17:03:37] Intel Quick Sync Video support: no [17:03:37] hb_scan: path=D:\[GM-Team][??][????][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][??][????][Xue Ying Ling Zhu][01][????][1080P].mp4, title_index=1 udfread ERROR: ECMA 167 Volume Recognition failed src/libbluray/disc/disc.c:333: failed opening UDF image D:\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01][‚"<ŠS'†^?‚oý][1080P].mp4 src/libbluray/disc/disc.c:437: error opening file BDMV\index.bdmv src/libbluray/disc/disc.c:437: error opening file BDMV\BACKUP\index.bdmv src/libbluray/bluray.c:2646: nav_get_title_list(D:\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01][‚"<ŠS'†^?‚oý][1080P].mp4\) failed [17:03:37] bd: not a bd - trying as a stream/file instead libdvdread: Encrypted DVD support unavailable. libdvdread: Could not open input: libdvdread: Can't open D:\[GM-Team][??][????][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][??][????][Xue Ying Ling Zhu][01][????][1080P].mp4 for reading libdvdnav: vm: failed to open/read the DVD [17:03:37] dvd: not a dvd - trying as a stream/file instead Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'D:\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][†>«‘¬®][‚>¦‚1ø‚›+„,¯][Xue Ying Ling Zhu][01][‚"<ŠS'†^?‚oý][1080P].mp4': Metadata: major_brand : isom minor_version : 512 compatible_brands: isomiso2avc1mp41 encoder : Lavf57.48.101 Duration: 00:20:26.86, start: 0.000000, bitrate: 1824 kb/s Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1920x1080, 1722 kb/s, 25 fps, 25 tbr, 90k tbn, 50 tbc (default) Metadata: handler_name : VideoHandler vendor_id : [0][0][0][0] Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 95 kb/s (default) Metadata: handler_name : SoundHandler vendor_id : [0][0][0][0] [17:03:37] scan: decoding previews for title 1 [17:03:37] scan: audio 0x1: aac, rate=48000Hz, bitrate=95672 Unknown (AAC LC) (2.0 ch) (95 kbps) [17:03:37] scan: 10 previews, 1920x1080, 25.000 fps, autocrop = 138/138/0/0, aspect 16:9, PAR 1:1, color profile: 1-1-1, chroma location: left [17:03:37] libhb: scan thread found 1 valid title(s) [17:03:37] Skipping subtitle scan. No suitable subtitle tracks. [17:03:37] Starting Task: Encoding Pass [17:03:37] [ass] libass API version: 0x1502000 [17:03:37] [ass] libass source: tarball: 0.15.2 [17:03:37] [ass] Shaper: FriBidi 1.0.11 (SIMPLE) HarfBuzz-ng 3.1.2 (COMPLEX) [17:03:37] [ass] Using font provider directwrite (with GDI) [17:03:37] work: track 1, dithering not supported by codec [17:03:37] work: only 1 chapter, disabling chapter markers [17:03:37] job configuration: [17:03:37] * source [17:03:37] + D:\[GM-Team][??][????][Xue Ying Ling Zhu][01-26][1080P]\[GM-Team][??][????][Xue Ying Ling Zhu][01][????][1080P].mp4 [17:03:37] + title 1, chapter(s) 1 to 1 [17:03:37] + container: mov,mp4,m4a,3gp,3g2,mj2 [17:03:37] + data rate: 1824 kbps [17:03:37] * destination [17:03:37] + C:\Users\Andre\Videos\[Gm-Team][??][????][Xue Ying Ling Zhu][01][????][1080P] 1.mp4 [17:03:37] + container: MPEG-4 (libavformat) [17:03:37] + align initial A/V stream timestamps [17:03:37] * video track [17:03:37] + decoder: h264 8-bit (yuv420p) [17:03:37] + bitrate 1722 kbps [17:03:37] + filters [17:03:37] + Comb Detect (mode=0:spatial-metric=2:motion-thresh=2:spatial-thresh=3:filter-mode=1:block-thresh=80:block-width=16:block-height=16) [17:03:37] + Decomb (mode=39) [17:03:37] + Framerate Shaper (mode=2:rate=27000000/900000) [17:03:37] + frame rate: 25.000 fps -> peak rate limited to 30.000 fps [17:03:37] + Subtitle renderer () [17:03:37] + Crop and Scale (width=1920:height=804:crop-top=138:crop-bottom=138:crop-left=0:crop-right=0) [17:03:37] + source: 1920 * 1080, crop (138/138/0/0): 1920 * 804, scale: 1920 * 804 [17:03:37] + Output geometry [17:03:37] + storage dimensions: 1920 x 804 [17:03:37] + pixel aspect ratio: 1 : 1 [17:03:37] + display dimensions: 1920 x 804 [17:03:37] + encoder: H.264 (libx264) [17:03:37] + preset: veryfast [17:03:37] + profile: main [17:03:37] + level: 4.0 [17:03:37] + quality: 24.00 (RF) [17:03:37] + color profile: 1-1-1 [17:03:37] + chroma location: left [17:03:37] * subtitle track 1, romŸna [SSA] (track 0, id 0xff000000, Text) -> Render/Burn-in, offset: 0 [17:03:37] * audio track 1 [17:03:37] + decoder: Unknown (AAC LC) (2.0 ch) (95 kbps) (track 1, id 0x1) [17:03:37] + bitrate: 95 kbps, samplerate: 48000 Hz [17:03:37] + mixdown: Stereo [17:03:37] + encoder: AAC (libavcodec) [17:03:37] + bitrate: 160 kbps, samplerate: 48000 Hz [17:03:37] sync: expecting 30671 video frames [17:03:37] encx264: encoding at constant RF 24.000000 [17:03:37] encx264: unparsed options: level=4.0:ref=1:8x8dct=0:weightp=1:subme=2:mixed-refs=0:trellis=0:vbv-bufsize=25000:vbv-maxrate=20000:rc-lookahead=10 x264 [info]: using SAR=1/1 x264 [info]: using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 x264 [info]: profile Main, level 4.0, 4:2:0, 8-bit [17:03:37] sync: first pts video is 0 [17:03:37] sync: "Chapter 1" (1) at frame 1 time 0 [17:03:37] sync: first pts audio 0x1 is 0 [17:03:38] sync: first pts subtitle 0xff000000 is 270000 [17:03:38] [ass] fontselect: (Arial, 400, 0) -> ArialMT, 0, ArialMT [17:03:44] [ass] fontselect: (Bahnschrift, 700, 0) -> Bahnschrift-Bold, 0, Bahnschrift-Bold [17:04:01] [ass] fontselect: (Palatino Linotype, 700, 0) -> PalatinoLinotype-Bold, 0, PalatinoLinotype-Bold [17:04:19] [ass] fontselect: (RO Bienetresocial, 700, 0) -> ROBienetresocial, 0, ROBienetresocial [17:04:47] [ass] fontselect: (BRUSHSTRIKE, 400, 100) -> BRUSHSTRIKE, 0, BRUSHSTRIKE [17:04:49] Worker process exited! [17:04:49] Worker process exit was not expected. # Job Failed (-12)

This project enhances SAR (Synthetic Aperture Radar) imagery by colorizing grayscale images using a CNN trained in the Lab color space. It features a Tkinter-based GUI for real-time image processing, dataset selection, and output preview. The system boosts SAR image interpretability for remote sensing and geospatial analysis.

This project aims to enhance Synthetic Aperture Radar (SAR) imagery by developing a deep learning-based system that colorizes grayscale SAR images and extracts meaningful features. The solution integrates advanced feature extraction and natural language processing (NLP) for prompt-based user interactions, similar to ChatGPT.

An AI-powered Smart India Hackathon 2024 project by Team NEXTECH. This deep learning-based solution enhances the interpretability of SAR (Synthetic Aperture Radar) imagery by intelligently colorizing grayscale images using advanced neural networks for better analysis in remote sensing and space technology.

No description available

No description available

No description available

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This project implements a deep learning model to colorize grayscale SAR images, enhancing their interpretability for applications like remote sensing and environmental monitoring. By leveraging paired SAR and optical data, the model transforms monochromatic SAR imagery into intuitive color representations, improving the usability of SAR data.

Deep learning model for SAR-to-Optical image translation using Pix2Pix (U-Net + PatchGAN). Pre-trained on Sentinel-1/2, fine-tuned on QXSLAB_SAROPT.

Developing a deep learning-based model to enhance grayscale Synthetic Aperture Radar (SAR) images by adding realistic color, improving visualization and interpretation for various applications.

The project uses Generative Adversarial Networks (GANs) to convert grayscale Synthetic Aperture Radar (SAR) images into visually meaningful RGB images, improving interpretability for analysis, surveillance, and disaster management.

Synthetic Aperture Radar (SAR) image converter that transforms SAR images into RGB format using a Generative Adversarial Network (GAN). The model employs the pix2pix framework with a U-Net architecture featuring skip connections with suitable generator and discriminator.

This repository implements a paired image-to-image translation pipeline for colorizing Synthetic Aperture Radar (SAR) images into RGB using a Pix2Pix-style architecture. It uses a U-Net Generator + PatchGAN Discriminator (PyTorch) with SAR preprocessing (dB scaling) using a paired dataset loader with random flips and rotations.

No description available

Deep learning project for colorizing Synthetic Aperture Radar (SAR) images using PyTorch and GANs. Transforms grayscale SAR images into realistic RGB representations using encoder-decoder architecture.

No description available

Ai based Synthetic Aperture Radar (SAR) images colorization

A simple Deep learning model used for colorizing SAR images.

Innovative SAR Image Colorization using a diffusion-based encoder-decoder in PyTorch. Converts grayscale SAR images to realistic RGB, enhancing interpretability. Supports training on paired datasets, single/multi-image prediction, and automated grayscale input generation—bridging SAR analysis with deep learning.

Developed Coloursar, a deep learning-based SAR image colorization program using U-Net CNN architecture on EuroSAT dataset.

**AI-Powered SAR Colorization for Disaster Prediction** This project enhances disaster risk prediction by converting grayscale SAR images to color using AI models like GANs and Transformers. Integrating Sentinel-1 SAR and Sentinel-2 Optical data, it improves flood, landslide, and wildfire forecasts, with Gradio and Flask.

This project applies diffusion models for SAR image colorization in PyTorch, turning grayscale radar data into realistic RGB images. Features include a custom encoder-decoder pipeline, dataset preparation tools, training with MSE loss, and flexible single/multi-image prediction support.

This project focuses on colorizing grayscale Synthetic Aperture Radar (SAR) images using Deep Learning models trained on paired SAR and Optical data. The goal is to improve SAR data interpretability for geological and environmental applications by predicting natural colors, enhancing visual analysis and insights.

SAR IMAGE COLORIZATION