Erin Byrne, MathWorks
This video walks through a typical image processing workflow example to analyze deforestation and the impact of conservation efforts on the Amazon rainforest. You will see how to import and display images. You will learn about pixel values, image histograms, and functions in the toolbox to help you work with them. You will be introduced to the various apps in Image Processing Toolbox™ and learn how to use the Image Segmenter and Color Thresholder apps to segment deforested areas in the images. You will also learn about generating reusable MATLAB® functions from these apps. Finally, you will learn about functions like regionprops that measure properties of image regions and use this to calculate area of deforestation.
Image processing is all about enhancing and extracting information from an image, to analyze and understand our world better. In this video, we'll walk through a typical image processing workflow. We'll use MATLAB and Image Processing Toolbox to analyze deforestation in the Amazon rainforest.
The Amazon rainforest is the largest and most diverse rainforest in the world. These are satellite images of the Jamanxim National Forests in Brazil, which has one of the highest rates of deforestation in the Amazon. But there have been significant efforts in recent years to slow the deforestation. Let's analyze images taken every four years of the same region to see the impact of those conservation efforts.
First, we'll have to identify the deforested regions in each image, and then calculate their area in pixels. We can then use this scale to convert the area from pixels to square kilometers. Let's head over to MATLAB to get started.
We have five images to work with, representing various years. Let's import the one from 2000. When you bring the image into the workspace, the name of the variable will match the name of the file by default. We'll use a live script to document, type, and run our code. To display an image in MATLAB, you can use the function imshow. Let's also add a simple title to our image.
This image as a height of 800 pixels and a width of 720 pixels. In MATLAB, this means we have an array with 800 rows and 720 columns. Color images are 3D arrays because we need to store the red, green, and blue values for each pixel. These values are integers that range from 0 to 255.
Let's compare 2000 to 2016. You can use the function imread to directly important image into the workspace. To display these images together, use imshowpair with the montage option. We can already see that the deforested area is larger in 2016. But to compare the two more accurately, we'll need to isolate the deforested regions.
In image processing, this is called segmentation. The deforested regions appear to be brighter. So we can use this brightness or intensity for segmentation.
It's easier to visualize intensities in a grayscale image. You can use im2gray to convert an image to grayscale. Grayscale images are 2D arrays because we only need one value, the intensity for each pixel. One way to examine these intensities is to create an intensity histogram.
The x-axis represents the intensity values. And the y-axis is the pixel count. Most of the intensities are clustered here, making it hard to isolate the bright, deforested regions from the darker surrounding areas. We'll want to adjust this histogram. So we use more pixels that are darker and brighter, which should increase the contrast of the image.
The function imadjust spreads the pixels out to match the intensity range better, while still maintaining the original shape. Comparing the original and adjusted images, the deforested regions are brighter and more prominent. Now, we need to decide which pixels belong to the deforested areas. One way is to threshold the intensity values.
If the intensity of the pixel is higher than the threshold, then it's deforested. If it's lower, it's not. Picking a good threshold is tricky. To find the right one, you can use the Image Segmenter app. You can find it in the Apps tab and the Image Processing and Computer Vision section.
We'll start by loading our image. We'll use the adjusted grayscale image from 2016. We already adjusted the contrast, so we don't have to do it again. Let's start with a Threshold tool. The blue areas indicate what will be segmented based on your current threshold settings. You can choose your method here.
The default tries to automatically pick an optimal threshold. But you can also manually choose a threshold with this option A low threshold isolates the deforested areas well, but also falsely segments some areas that aren't deforested. A high threshold removes those false segmentations, but then you lose some areas that are deforested. It's all about balance. In this case, a threshold around 100 does a good job.
To view the result, Click Show Binary. It looks pretty good. But we can see a few small problems. For example, this squiggly line is not a deforested region, but a river that shouldn't be segmented.
We can refine our segmentation using these options. Here, let's try Morphology, which will allow us to modify the shape of the segmented areas. Choose the type of operation here. Since we want to remove the river, let's try the Open Mask operation, which removes areas smaller than a given shape.
For the shape, choose one that resembles the areas you want to keep. In our case, the areas are square. Next, set a size for the shape. If it's too small, then you might not remove the river, but if it's too big, you'll remove deforested areas. In this case, a length of 3 removes the river and leaves the rest mostly intact.
OK, we've segmented one image. So let's try another one. How about 2004?
We'll go back to our live script and import it, convert it to grayscale, and adjust the contrast. Then back in the app, let's import the adjusted image. It looks like the same threshold also segments most of the deforested areas. However, this image contains some clouds from the atmosphere that block our view.
Those clouds are falsely segmented, due to being very bright. Also, the text in the corners is being segmented. This was the case in the 2016 image too. It's not a big deal, but it'd be nice if we could remove it.
So it looks like we're not going to be able to use intensity to segment this image. How about color? The deforested areas have a distinct brown color that's different from the white and dark green colors in the rest of the image. We can segment based on color using the Color Thresholder app. Let's use the image from 2016, because it has more of that brown color that we're looking for.
Next, choose the color space to segment in. Different color spaces work better for different images. So how do we choose? Well, these point clouds can help.
They visualize the colors in a 3D space. You'll want to pick the one that isolates the brown color the most. These three are all equally good. So let's just pick one and start. How about HSV?
Hue determines the color. Saturation sets the shade. And value describes the brightness. Let's start with hue.
We can remove most of the green color with this alone. As you move the slider, the preview and point clouds update in real time. This still leaves parts of the year text visible. Let's zoom in and get a closer look.
The remaining text is white, which is hard to remove using hue. Instead, we can use the fact that it's very bright and use the Value Slider. So filter out the brightest pixels, and it removes the remaining text.
OK, let's go back and look at the binary image. Well, that looks like a pretty good segmentation. Now we're ready to export the results. You can export the image itself, but since we're going to do this for multiple images, let's generate a function instead.
The generated function takes in the RGB image as the input. And it has two outputs-- the mast image, which is the preview from the app, and BW, which is the binary image. Let's save our function so that we can use it in our script.
We'll save it as createMask in our current folder. All right, let's try it out on the image from 2004. Last time, the clouds got in our way. This time, it looks like our function was able to remove the clouds while preserving the deforested areas.
Now that we have our segmentation, it's time to calculate the area of the deforested regions. The regionprops function calculates a lot of useful information about the segmented regions in an image. We're only interested in the area of each region. So let's use the Area option. Then, to get the total area, we'll add them all up.
Now, this area is in pixels. So we need to convert it to square kilometers. Remember that scale in the images? We can use that.
To measure the scale, we can use the Image Viewer app. All right, let's Zoom in. The app has a ruler tool that we can use to measure the length of this line. It looks like 20 kilometers is about 51 pixels.
Back in our script, we can calculate the conversion factor. Then use that to find the total deforested area in square kilometers. Now, we'll need to do this for all the images, which we can do using the Image Batch Processor app.
For this app, you'll have to choose the folder that contains your images. Once they're loaded, you'll see a preview of all the images to be processed. Next, select the function to process each image. If you click New, the app generates a template to help you write your own function I've already written a processing function called Calculate Area. Let's take a look at it.
To work with the app, the function must have an image as the input, and any outputs formatted as a structure. Here, we'll output both the binary image and the area in square kilometers. Back in the app, we can browse for that function and process all the images.
The results appear over here. For each image, we can preview the segmentation and the area. Let's export to the workspace so we can analyze the results further. We'll include everything in a table named allresults.
We can see the deforested area increases over the years, but not at a constant rate. Let's create a bar chart of the areas so we can see the trend. There was a considerable jump between 2004 and 2008. After that, there's a significant reduction in the deforestation rate, showing the impact of conservation efforts.
We can see this in the segment and images too. Use the montage function to view a collection of images. The segmented areas expand in 2008, then remain fairly constant in the next two images. It's pretty cool to see how conservation efforts really can make a difference.
These files are available to download, so you can try it out for yourself. We've shown you some of the capabilities of Image Processing Toolbox, but there's so much more to explore. Our images focused on one small part of the Amazon. If you had more images of the surrounding regions, you could use image registration to stitch the images together. Or if you had multispectral images, you could process them to identify different types of vegetation.
The sky is the limit. Check out the Image Processing Onramp or the Image Processing with MATLAB course to learn more. Welcome to Image Processing with MATLAB.
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